From 3afc4bd6418b8bb42eafda652cc0403955af093b Mon Sep 17 00:00:00 2001
From: "FAZELI SHAHROUDI Sepehr (INTERN)"
 <Sepehr.FAZELISHAHROUDI.intern@3ds.com>
Date: Mon, 24 Mar 2025 00:38:58 +0100
Subject: [PATCH] update: introduction updated

---
 Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl     | 148 ++++++++
 chapters/1-Introduction.tex                   |   4 +-
 .../Chapter-1-sections/Aim-and-Objectives.tex |  36 +-
 .../General-Introduction.tex                  |  78 ++--
 sections/Chapter-1-sections/Related-Work.tex  |   4 +-
 sections/Chapter-1-sections/Relevance.tex     |  63 +--
 sources/references.bib                        | 359 ++++++++++++++++++
 7 files changed, 595 insertions(+), 97 deletions(-)

diff --git a/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl b/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl
index 8d44975..573d9c7 100644
--- a/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl
+++ b/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl
@@ -21,6 +21,142 @@
 \providecommand{\BIBdecl}{\relax}
 \BIBdecl
 
+\bibitem{gonzalez_digital_2008-1}
+R.~C. Gonzalez and R.~E. Woods, \emph{\BIBforeignlanguage{en}{Digital {Image}
+  {Processing}}}.\hskip 1em plus 0.5em minus 0.4em\relax Prentice Hall, 2008,
+  google-Books-ID: 8uGOnjRGEzoC.
+
+\bibitem{jain_fundamentals_1989-1}
+\BIBentryALTinterwordspacing
+A.~K. Jain, \emph{\BIBforeignlanguage{eng}{Fundamentals of digital image
+  processing}}.\hskip 1em plus 0.5em minus 0.4em\relax Englewood Cliffs, NJ :
+  Prentice Hall, 1989. [Online]. Available:
+  \url{http://archive.org/details/fundamentalsofdi0000jain}
+\BIBentrySTDinterwordspacing
+
+\bibitem{russ_image_2016}
+J.~C. Russ, \emph{\BIBforeignlanguage{en}{The {Image} {Processing}
+  {Handbook}}}.\hskip 1em plus 0.5em minus 0.4em\relax CRC Press, Apr. 2016,
+  google-Books-ID: gxXXRJWfEsoC.
+
+\bibitem{szeliski_introduction_2022}
+\BIBentryALTinterwordspacing
+R.~Szeliski, ``\BIBforeignlanguage{en}{Introduction},'' in
+  \emph{\BIBforeignlanguage{en}{Computer {Vision}: {Algorithms} and
+  {Applications}}}, R.~Szeliski, Ed.\hskip 1em plus 0.5em minus 0.4em\relax
+  Cham: Springer International Publishing, 2022, pp. 1--26. [Online].
+  Available: \url{https://doi.org/10.1007/978-3-030-34372-9_1}
+\BIBentrySTDinterwordspacing
+
+\bibitem{goodfellow_deep_2016}
+I.~Goodfellow, Y.~Bengio, and A.~Courville, \emph{\BIBforeignlanguage{en}{Deep
+  {Learning}}}.\hskip 1em plus 0.5em minus 0.4em\relax MIT Press, Nov. 2016,
+  google-Books-ID: Np9SDQAAQBAJ.
+
+\bibitem{bradski_learning_2008}
+\BIBentryALTinterwordspacing
+G.~R. Bradski, \emph{\BIBforeignlanguage{eng}{Learning {OpenCV} : computer
+  vision with the {OpenCV} library}}.\hskip 1em plus 0.5em minus 0.4em\relax
+  Sebastopol, CA : O'Reilly, 2008. [Online]. Available:
+  \url{http://archive.org/details/learningopencvco0000brad}
+\BIBentrySTDinterwordspacing
+
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+\BIBentryALTinterwordspacing
+J.~W. Cooley and J.~W. Tukey, ``An {Algorithm} for the {Machine} {Calculation}
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+  vol.~19, no.~90, pp. 297--301, 1965, publisher: American Mathematical
+  Society. [Online]. Available: \url{https://www.jstor.org/stable/2003354}
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+  no. 7553, pp. 436--444, 2015, place: United Kingdom Publisher: Nature
+  Publishing Group.
+
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+G.~E. Hinton, N.~Srivastava, A.~Krizhevsky, I.~Sutskever, and R.~R.
+  Salakhutdinov, ``Improving neural networks by preventing co-adaptation of
+  feature detectors,'' Jul. 2012, arXiv:1207.0580 [cs]. [Online]. Available:
+  \url{http://arxiv.org/abs/1207.0580}
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+D.~Zhang, X.~Hao, D.~Wang, C.~Qin, B.~Zhao, L.~Liang, and W.~Liu,
+  ``\BIBforeignlanguage{en}{An efficient lightweight convolutional neural
+  network for industrial surface defect detection},''
+  \emph{\BIBforeignlanguage{en}{Artificial Intelligence Review}}, vol.~56,
+  no.~9, pp. 10\,651--10\,677, Sep. 2023. [Online]. Available:
+  \url{https://doi.org/10.1007/s10462-023-10438-y}
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+G.~Litjens, T.~Kooi, B.~E. Bejnordi, A.~A.~A. Setio, F.~Ciompi, M.~Ghafoorian,
+  J.~A. W. M. v.~d. Laak, B.~v. Ginneken, and C.~I. Sánchez, ``A {Survey} on
+  {Deep} {Learning} in {Medical} {Image} {Analysis},'' \emph{Medical Image
+  Analysis}, vol.~42, pp. 60--88, Dec. 2017, arXiv:1702.05747 [cs]. [Online].
+  Available: \url{http://arxiv.org/abs/1702.05747}
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+
+\bibitem{maimaitijiang_soybean_2020}
+\BIBentryALTinterwordspacing
+M.~Maimaitijiang, ``Soybean yield prediction from {UAV} using multimodal data
+  fusion and deep learning,'' \emph{Remote Sensing of Environment}, Jan. 2020.
+  [Online]. Available:
+  \url{https://www.academia.edu/84238554/Soybean_yield_prediction_from_UAV_using_multimodal_data_fusion_and_deep_learning}
+\BIBentrySTDinterwordspacing
+
+\bibitem{janai_computer_2021}
+\BIBentryALTinterwordspacing
+J.~Janai, F.~Güney, A.~Behl, and A.~Geiger, ``Computer {Vision} for
+  {Autonomous} {Vehicles}: {Problems}, {Datasets} and {State} of the {Art},''
+  Mar. 2021, arXiv:1704.05519 [cs]. [Online]. Available:
+  \url{http://arxiv.org/abs/1704.05519}
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+\bibitem{ren_faster_2016}
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+S.~Ren, K.~He, R.~Girshick, and J.~Sun, ``Faster {R}-{CNN}: {Towards}
+  {Real}-{Time} {Object} {Detection} with {Region} {Proposal} {Networks},''
+  Jan. 2016, arXiv:1506.01497 [cs]. [Online]. Available:
+  \url{http://arxiv.org/abs/1506.01497}
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+
+\bibitem{ragan-kelley_halide_2013}
+J.~Ragan-Kelley, C.~Barnes, A.~Adams, S.~Paris, F.~Durand, and S.~Amarasinghe,
+  \emph{Halide: {A} {Language} and {Compiler} for {Optimizing} {Parallelism},
+  {Locality}, and {Recomputation} in {Image} {Processing} {Pipelines}}, Jun.
+  2013, vol.~48, journal Abbreviation: ACM SIGPLAN Notices Pages: 530
+  Publication Title: ACM SIGPLAN Notices.
+
+\bibitem{szeliski_image_2022}
+\BIBentryALTinterwordspacing
+R.~Szeliski, ``\BIBforeignlanguage{en}{Image {Processing}},'' in
+  \emph{\BIBforeignlanguage{en}{Computer {Vision}: {Algorithms} and
+  {Applications}}}, R.~Szeliski, Ed.\hskip 1em plus 0.5em minus 0.4em\relax
+  Cham: Springer International Publishing, 2022, pp. 85--151. [Online].
+  Available: \url{https://doi.org/10.1007/978-3-030-34372-9_3}
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+
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+\BIBentryALTinterwordspacing
+S.~J. Russell, P.~Norvig, E.~Davis, and D.~Edwards, \emph{Artificial
+  intelligence a modern approach}, third edition, global edition~ed.\hskip 1em
+  plus 0.5em minus 0.4em\relax Boston: Pearson, 2016. [Online]. Available:
+  \url{http://www.gbv.de/dms/tib-ub-hannover/848811429.pdf}
+\BIBentrySTDinterwordspacing
+
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 Z.~Kulpa, ``\BIBforeignlanguage{en}{Universal digital image processing systems
   in europe — {A} comparative survey},'' in
@@ -28,6 +164,14 @@ Z.~Kulpa, ``\BIBforeignlanguage{en}{Universal digital image processing systems
   L.~Bloc and Z.~Kulpa, Eds.\hskip 1em plus 0.5em minus 0.4em\relax Berlin,
   Heidelberg: Springer, 1981, pp. 1--20.
 
+\bibitem{sahebi_distributed_2023}
+\BIBentryALTinterwordspacing
+A.~Sahebi, M.~Barbone, M.~Procaccini, W.~Luk, G.~Gaydadjiev, and R.~Giorgi,
+  ``Distributed large-scale graph processing on {FPGAs},'' \emph{Journal of Big
+  Data}, vol.~10, no.~1, p.~95, Jun. 2023. [Online]. Available:
+  \url{https://doi.org/10.1186/s40537-023-00756-x}
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@@ -53,6 +197,10 @@ D.~Ferreira, F.~Moutinho, J.~P. Matos-Carvalho, M.~Guedes, and P.~Deusdado,
   \emph{\BIBforeignlanguage{eng}{Sensors (Basel, Switzerland)}}, vol.~24,
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+\bibitem{lai_image_2001}
+B.-C. Lai, {Phillip}, and P.~McKerrow, ``Image {Processing} {Libraries},'' Jan.
+  2001.
+
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 \BIBentryALTinterwordspacing
 J.~Pérez, E.~Magdaleno, F.~Pérez, M.~Rodríguez, D.~Hernández, and
diff --git a/chapters/1-Introduction.tex b/chapters/1-Introduction.tex
index b4f51b4..dc7f450 100755
--- a/chapters/1-Introduction.tex
+++ b/chapters/1-Introduction.tex
@@ -2,10 +2,10 @@
 
 \input{sections/Chapter-1-sections/General-Introduction.tex}
 
-\input{sections/Chapter-1-sections/Relevance.tex}
+% \input{sections/Chapter-1-sections/Relevance.tex}
 
 \input{sections/Chapter-1-sections/Aim-and-Objectives.tex}
 
-\input{sections/Chapter-1-sections/Research-Questions.tex}
+% \input{sections/Chapter-1-sections/Research-Questions.tex}
 
 \input{sections/Chapter-1-sections/Related-Work.tex}
\ No newline at end of file
diff --git a/sections/Chapter-1-sections/Aim-and-Objectives.tex b/sections/Chapter-1-sections/Aim-and-Objectives.tex
index 4716845..8611495 100644
--- a/sections/Chapter-1-sections/Aim-and-Objectives.tex
+++ b/sections/Chapter-1-sections/Aim-and-Objectives.tex
@@ -1,6 +1,6 @@
 \section{ Aim of the Study and Its Implications for Selecting an Image Processing Tool}
 
-This study was initiated to compare a broad range of image processing libraries based on performance, functionality, and ease of integration. Although the investigation was partly motivated by considerations around the ImageSharp license, the primary goal is to establish a general framework for evaluating different tools in the field. By assessing key metrics such as image conversion speed, pixel iteration efficiency, memory consumption, and development effort, the research aims to provide a balanced perspective that assists developers, engineers, and decision-makers in selecting the most appropriate image processing tool for their projects.
+The purpose of this study was to compare the performance, functionality, and ease of integration of a wide range of image processing libraries. The primary objective is to establish a general framework for evaluating different tools in the field. As part of this research, key metrics such as conversion speed, pixel iteration efficiency, memory consumption, and development effort will be evaluated in order to provide developers, engineers, and decision-makers with a balanced viewpoint.
 
 \subsection{ Research Goals and Objectives}
 
@@ -17,26 +17,26 @@ At its core, the study sought to answer the question: “Which image processing
         Beyond performance metrics, the study was designed to consider the broader context of software integration. Factors such as ease of implementation, the learning curve for developers, compatibility with existing systems, and community support were all taken into account. This holistic view means that the research is not just about raw performance numbers but also about the practicalities of deploying and maintaining these tools in production environments.
 \end{enumerate}
 
-\subsection{ Methodology and Benchmarking}
+% \subsection{ Methodology and Benchmarking}
 
-To achieve these objectives, the study adopted a multi-faceted methodological approach that combined qualitative assessments with quantitative benchmarks. The research was structured into several key phases:
+% To achieve these objectives, the study adopted a multi-faceted methodological approach that combined qualitative assessments with quantitative benchmarks. The research was structured into several key phases:
 
-\subsubsection{ Establishing Functional Criteria}
+% \subsubsection{ Establishing Functional Criteria}
 
-The first step was to outline the core functionalities required from an image processing library. These functionalities included image loading and creation, pixel-level manipulation, image transformation (such as cropping, resizing, and color conversion), and the encoding\\decoding of various image formats. Each library was then evaluated on how well it supports these functions. For example, while ImageSharp provides an elegant and fluent API for chaining operations like cloning, mutating, and resizing images, other tools like Emgu CV or SkiaSharp may offer advantages in raw performance or specific tasks such as advanced 2D rendering.
+% The first step was to outline the core functionalities required from an image processing library. These functionalities included image loading and creation, pixel-level manipulation, image transformation (such as cropping, resizing, and color conversion), and the encoding\\decoding of various image formats. Each library was then evaluated on how well it supports these functions. For example, while ImageSharp provides an elegant and fluent API for chaining operations like cloning, mutating, and resizing images, other tools like Emgu CV or SkiaSharp may offer advantages in raw performance or specific tasks such as advanced 2D rendering.
 
-\subsubsection{ Performance and Memory Benchmarking}
+% \subsubsection{ Performance and Memory Benchmarking}
 
-Quantitative performance metrics were a central component of the study. Two key tests were developed:
+% Quantitative performance metrics were a central component of the study. Two key tests were developed:
 
-\begin{itemize}
-    \item \textbf{Image Conversion Test:} This test measured the time taken to load an image, convert it to a different format, and save the result. It simulates a typical workflow in many image processing applications and serves as a proxy for real-world performance. The results indicated significant differences between libraries. For instance, SkiaSharp showed excellent performance in image conversion tasks with both the fastest conversion times and minimal memory allocation, making it an attractive option for performance-critical applications.
-    \item \textbf{Pixel Iteration Test:} Many image processing tasks require iterating over each pixel—for example, when applying filters or performing color adjustments. The study evaluated how long each library took to perform such operations and the associated memory footprint. Although some tools demonstrated faster pixel iteration times, the overall memory consumption varied widely, highlighting the trade-off between speed and resource usage.
-\end{itemize}
+% \begin{itemize}
+%     \item \textbf{Image Conversion Test:} This test measured the time taken to load an image, convert it to a different format, and save the result. It simulates a typical workflow in many image processing applications and serves as a proxy for real-world performance. The results indicated significant differences between libraries. For instance, SkiaSharp showed excellent performance in image conversion tasks with both the fastest conversion times and minimal memory allocation, making it an attractive option for performance-critical applications.
+%     \item \textbf{Pixel Iteration Test:} Many image processing tasks require iterating over each pixel—for example, when applying filters or performing color adjustments. The study evaluated how long each library took to perform such operations and the associated memory footprint. Although some tools demonstrated faster pixel iteration times, the overall memory consumption varied widely, highlighting the trade-off between speed and resource usage.
+% \end{itemize}
 
-\subsubsection{ Estimation of Development Effort}
+% \subsubsection{ Estimation of Development Effort}
 
-Recognizing that performance is not the sole criterion for tool selection, the study also estimated the development effort required to integrate each library into an existing application. This included considerations such as the ease of understanding the API, the availability of documentation and community support, and the potential need for custom code to bridge functionality gaps. For example, while some libraries offered powerful processing capabilities, they might require significant custom development to integrate seamlessly into a .NET environment or to support specific image formats.
+% Recognizing that performance is not the sole criterion for tool selection, the study also estimated the development effort required to integrate each library into an existing application. This included considerations such as the ease of understanding the API, the availability of documentation and community support, and the potential need for custom code to bridge functionality gaps. For example, while some libraries offered powerful processing capabilities, they might require significant custom development to integrate seamlessly into a .NET environment or to support specific image formats.
 
 \subsection{ Practical Implications for Tool Selection}
 
@@ -52,16 +52,16 @@ One of the standout contributions of the study is its ability to help users make
 
 \subsubsection{ Extending Beyond Cost Savings}
 
-While cost savings—such as the €5000 per year saving associated with avoiding ImageSharp’s licensing fees—are certainly a factor, the study underscores that financial considerations should not be the sole driver of decision-making. The true value of an image processing tool lies in its ability to meet specific technical and operational requirements. By providing a detailed comparison of several alternatives, the research emphasizes that factors like ease of integration, scalability, and overall performance are equally, if not more, important. This holistic approach helps organizations avoid the pitfall of selecting a tool based solely on its cost.
+While cost savings are certainly a factor, the study underscores that financial considerations should not be the sole driver of decision-making. The true value of an image processing tool lies in its ability to meet specific technical and operational requirements. By providing a detailed comparison of several alternatives, the research emphasizes that factors like ease of integration, scalability, and overall performance are equally, if not more, important. This holistic approach helps organizations avoid the pitfall of selecting a tool based solely on its cost.
 
 \subsubsection{ Guiding Future Developments and Integrations}
 
 The insights gained from the study are not only applicable to current technology choices but also serve as a guide for future developments in image processing. The detailed benchmarks and performance analyses can inform future projects, helping developers understand where improvements can be made or which features are most critical. Additionally, the study’s approach to evaluating development effort and integration challenges provides a roadmap for how future research can build on these findings to further refine the selection process.
 
-\subsection{ Conclusion}
+% \subsection{ Conclusion}
 
-In conclusion, this research offers a detailed and methodical framework for comparing a diverse range of image processing libraries. By focusing on critical performance indicators—such as image conversion efficiency, pixel manipulation speed, and memory usage—alongside practical considerations for integration, the study provides actionable insights that transcend mere numerical comparisons. This comprehensive evaluation enables practitioners to appreciate the subtle differences and inherent trade-offs between various tools, ensuring that the selected library meets specific operational requirements and supports long-term scalability.
+% In conclusion, this research offers a detailed and methodical framework for comparing a diverse range of image processing libraries. By focusing on critical performance indicators—such as image conversion efficiency, pixel manipulation speed, and memory usage—alongside practical considerations for integration, the study provides actionable insights that transcend mere numerical comparisons. This comprehensive evaluation enables practitioners to appreciate the subtle differences and inherent trade-offs between various tools, ensuring that the selected library meets specific operational requirements and supports long-term scalability.
 
-The findings underscore the importance of adopting a multi-dimensional evaluation approach. Rather than basing decisions solely on isolated performance metrics, the research illustrates how a balanced view—integrating both technical capabilities and practical implementation challenges—can lead to more robust and adaptable solutions. This perspective is essential in a field where evolving technologies and shifting project demands necessitate both flexibility and precision in tool selection.
+% The findings underscore the importance of adopting a multi-dimensional evaluation approach. Rather than basing decisions solely on isolated performance metrics, the research illustrates how a balanced view—integrating both technical capabilities and practical implementation challenges—can lead to more robust and adaptable solutions. This perspective is essential in a field where evolving technologies and shifting project demands necessitate both flexibility and precision in tool selection.
 
-Ultimately, the insights derived from this investigation empower developers, engineers, and decision-makers to navigate the complex landscape of image processing technologies with confidence. By providing a thorough, balanced comparison of various libraries, the study serves as a valuable resource for making informed decisions that address current needs while also laying a strong foundation for future innovation and development in image processing.
\ No newline at end of file
+% Ultimately, the insights derived from this investigation empower developers, engineers, and decision-makers to navigate the complex landscape of image processing technologies with confidence. By providing a thorough, balanced comparison of various libraries, the study serves as a valuable resource for making informed decisions that address current needs while also laying a strong foundation for future innovation and development in image processing.
\ No newline at end of file
diff --git a/sections/Chapter-1-sections/General-Introduction.tex b/sections/Chapter-1-sections/General-Introduction.tex
index 6d97723..b63168b 100644
--- a/sections/Chapter-1-sections/General-Introduction.tex
+++ b/sections/Chapter-1-sections/General-Introduction.tex
@@ -1,23 +1,33 @@
 \section{ The Significance of Image Processing in Modern Industry}
 
-Digital image processing has emerged as a cornerstone of modern industrial applications, revolutionizing the way industries operate and innovate. From quality control in manufacturing to advanced simulations in aerospace, the ability to process and analyze images digitally has unlocked unprecedented efficiencies and capabilities. This field, which involves the manipulation and analysis of images using algorithms, has evolved significantly over the past few decades, driven by advancements in computing power, algorithm development, and the proliferation of digital imaging devices.
+Digital image processing has emerged as a cornerstone of modern industrial applications, revolutionizing the way industries operate and innovate. From quality control in manufacturing to advanced simulations in aerospace, the ability to process and analyze images digitally has unlocked unprecedented efficiencies and capabilities. This field, which involves the manipulation and analysis of images using algorithms, has evolved significantly over the past few decades, driven by advancements in computing power, algorithm development, and the proliferation of digital imaging devices \cite{gonzalez_digital_2008-1,jain_fundamentals_1989-1}.
 
-The significance of digital image processing in industrial applications cannot be overstated. In manufacturing, for instance, image processing is integral to quality assurance processes, where it is used to detect defects, measure product dimensions, and ensure compliance with stringent standards. This capability not only enhances product quality but also reduces waste and operational costs. In the automotive industry, image processing is pivotal in the development of autonomous vehicles, where it aids in object detection, lane departure warnings, and pedestrian recognition. Similarly, in the healthcare sector, digital image processing is used in medical imaging technologies such as MRI and CT scans, enabling more accurate diagnoses and treatment planning.
+The significance of digital image processing in industrial applications cannot be overstated. In manufacturing, for instance, image processing is integral to quality assurance processes, where it is used to detect defects, measure product dimensions, and ensure compliance with stringent standards . This capability not only enhances product quality but also reduces waste and operational costs. In the automotive industry, image processing is pivotal in the development of autonomous vehicles, where it aids in object detection, lane departure warnings, and pedestrian recognition. Similarly, in the healthcare sector, digital image processing is used in medical imaging technologies such as MRI and CT scans, enabling more accurate diagnoses and treatment planning \cite{russ_image_2016,szeliski_introduction_2022}.
 
-The evolution of digital image processing has been marked by several key developments. Initially, the field was limited by the computational resources available, with early applications focusing on basic image enhancement and restoration. However, the advent of powerful processors and the development of sophisticated algorithms have expanded the scope of image processing to include complex tasks such as pattern recognition, 3D reconstruction, and real-time image analysis. The integration of artificial intelligence and machine learning has further propelled the field, allowing for the development of intelligent systems capable of learning from data and improving over time.
+The evolution of digital image processing has been marked by several key developments. Initially, the field was limited by the computational resources available, with early applications focusing on basic image enhancement and restoration. However, the advent of powerful processors and the development of sophisticated algorithms have expanded the scope of image processing to include complex tasks such as pattern recognition, 3D reconstruction, and real-time image analysis. The integration of artificial intelligence and machine learning has further propelled the field, allowing for the development of intelligent systems capable of learning from data and improving over time \cite{gonzalez_digital_2008-1,szeliski_introduction_2022,goodfellow_deep_2016}.
 
-For industries like Dassault Systems, which operates at the forefront of aerospace, defense, and industrial engineering, a comparative study of image processing libraries is crucial. These libraries, which provide pre-built functions and tools for image analysis, vary significantly in terms of performance, ease of use, and functionality. Selecting the right library can have a profound impact on the efficiency and effectiveness of image processing tasks. For instance, libraries such as OpenCV, Imagemagick and ImageSharp offer different strengths and weaknesses, and understanding these differences is essential for optimizing industrial applications.
+For industries like Dassault Systems, which operates at the forefront of aerospace, defense, and industrial engineering, a comparative study of image processing libraries is crucial. These libraries, which provide pre-built functions and tools for image analysis, vary significantly in terms of performance, ease of use, and functionality. Selecting the right library can have a profound impact on the efficiency and effectiveness of image processing tasks. For instance, libraries such as OpenCV, Imagemagick and ImageSharp offer different strengths and weaknesses, and understanding these differences is essential for optimizing industrial applications \cite{bradski_learning_2008}.
 
 A comparative study of these libraries not only aids in selecting the most suitable tools for specific tasks but also highlights areas for potential improvement and innovation. By analyzing the performance of different libraries in various scenarios, industries can identify gaps in current technologies and drive the development of new solutions that better meet their needs. Moreover, such studies contribute to the broader field of digital image processing by providing insights into best practices and emerging trends.
 
 % References
 
+
 % 1. Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing. Pearson Prentice Hall.
+% \cite{gonzalez_digital_2008}
+% \cite{gonzalez_digital_2008-1}
 % 2. Jain, A. K. (1989). Fundamentals of Digital Image Processing. Prentice Hall.
+% \cite{jain_fundamentals_1989}
+% \cite{jain_fundamentals_1989-1}
 % 3. Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media.
-% 4. Russ, J. C. (2011). The Image Processing Handbook. CRC Press.
+% \cite{bradski_learning_2008}
+% 4. Russ, J. C. (2016). The Image Processing Handbook. CRC Press.
+% \cite{russ_image_2016}
 % 5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
-% 6. Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.
+% \cite{goodfellow_deep_2016}
+% 6. Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Springer.
+% \cite{szeliski_introduction_2022}
+% \cite{szeliski_image_2022}
 
 \subsection{Evolution and Impact of Digital Image Processing}
 
@@ -25,32 +35,37 @@ Digital image processing has evolved significantly since its inception, transfor
 
 \subsubsection{Early Beginnings}
 
-The origins of digital image processing can be traced back to the 1920s and 1930s with the development of television technology, which laid the groundwork for electronic image capture and transmission. However, it wasn't until the 1960s that digital image processing began to take shape as a distinct field. The launch of the first digital computers provided the necessary computational power to process images digitally. During this period, NASA played a pivotal role by using digital image processing to enhance images of the moon's surface captured by the Ranger 7 spacecraft in 1964. This marked one of the first significant applications of digital image processing, demonstrating its potential for scientific and exploratory purposes.
+The origins of digital image processing can be traced back to the 1920s and 1930s with the development of television technology, which laid the groundwork for electronic image capture and transmission. However, it wasn't until the 1960s that digital image processing began to take shape as a distinct field. The launch of the first digital computers provided the necessary computational power to process images digitally. During this period, NASA played a pivotal role by using digital image processing to enhance images of the moon's surface captured by the Ranger 7 spacecraft in 1964. This marked one of the first significant applications of digital image processing, demonstrating its potential for scientific and exploratory purposes \cite{cooley_algorithm_1965}.
 
 \subsubsection{The 1970s and 1980s: Theoretical Foundations and Practical Applications}
 
-The 1970s saw the establishment of theoretical foundations for digital image processing. Researchers developed algorithms for image enhancement, restoration, and compression. The Fast Fourier Transform (FFT), introduced by Cooley and Tukey in 1965, became a fundamental tool for image processing, enabling efficient computation of image transformations. This period also witnessed the development of the first commercial applications, such as medical imaging systems. The introduction of Computed Tomography (CT) in 1972 revolutionized medical diagnostics by providing detailed cross-sectional images of the human body, showcasing the life-saving potential of digital image processing.
+The 1970s saw the establishment of theoretical foundations for digital image processing. Researchers developed algorithms for image enhancement, restoration, and compression. The Fast Fourier Transform (FFT), introduced by Cooley and Tukey in 1965, became a fundamental tool for image processing, enabling efficient computation of image transformations. This period also witnessed the development of the first commercial applications, such as medical imaging systems. The introduction of Computed Tomography (CT) in 1972 revolutionized medical diagnostics by providing detailed cross-sectional images of the human body, showcasing the life-saving potential of digital image processing \cite{cooley_algorithm_1965,hounsfield_computerized_1973}.
 
 \subsubsection{The 1990s: The Rise of Computer Vision}
 
-The 1990s marked a significant shift towards computer vision, a subfield of digital image processing focused on enabling machines to interpret visual data. This era saw the development of algorithms for object recognition, motion detection, and 3D reconstruction. The introduction of the JPEG standard in 1992 facilitated the widespread adoption of digital images by providing an efficient method for image compression, crucial for the burgeoning internet era. The decade also saw advancements in facial recognition technology, which laid the groundwork for future applications in security and personal identification.
+The 1990s marked a significant shift towards computer vision, a subfield of digital image processing focused on enabling machines to interpret visual data. This era saw the development of algorithms for object recognition, motion detection, and 3D reconstruction. The introduction of the JPEG standard in 1992 facilitated the widespread adoption of digital images by providing an efficient method for image compression, crucial for the burgeoning internet era. The decade also saw advancements in facial recognition technology, which laid the groundwork for future applications in security and personal identification \cite{lecun_deep_2015}.
 
 \subsubsection{The 2000s: Machine Learning and Image Processing}
 
-The 2000s witnessed the integration of machine learning techniques with digital image processing, leading to significant improvements in image analysis and interpretation. The development of Support Vector Machines (SVM) and neural networks enabled more accurate image classification and pattern recognition. This period also saw the emergence of digital cameras and smartphones, which democratized image capture and sharing, further driving the demand for advanced image processing techniques.
+The 2000s witnessed the integration of machine learning techniques with digital image processing, leading to significant improvements in image analysis and interpretation. The development of Support Vector Machines (SVM) and neural networks enabled more accurate image classification and pattern recognition. This period also saw the emergence of digital cameras and smartphones, which democratized image capture and sharing, further driving the demand for advanced image processing techniques\cite{lecun_deep_2015}.
 
 \subsubsection{The 2010s to Present: Deep Learning and Industrial Innovation}
 
-The advent of deep learning in the 2010s revolutionized digital image processing. Convolutional Neural Networks (CNNs), popularized by the success of AlexNet in the ImageNet competition in 2012, dramatically improved the accuracy of image recognition tasks. This breakthrough spurred innovation across various industries. In healthcare, deep learning algorithms are now used for early detection of diseases through medical imaging, improving patient outcomes. In the automotive industry, image processing is a critical component of autonomous vehicle systems, enabling real-time object detection and navigation.
+The advent of deep learning in the 2010s revolutionized digital image processing. Convolutional Neural Networks (CNNs), popularized by the success of AlexNet in the ImageNet competition in 2012, dramatically improved the accuracy of image recognition tasks. This breakthrough spurred innovation across various industries. In healthcare, deep learning algorithms are now used for early detection of diseases through medical imaging, improving patient outcomes. In the automotive industry, image processing is a critical component of autonomous vehicle systems, enabling real-time object detection and navigation \cite{hinton_improving_2012,lecun_deep_2015}.
 
 In recent years, digital image processing has expanded into areas such as augmented reality (AR) and virtual reality (VR), enhancing user experiences in gaming, education, and training. The integration of image processing with artificial intelligence continues to drive innovation, with applications in fields such as agriculture, where drones equipped with image processing capabilities monitor crop health and optimize yields.
 
 % References
 
 % 1. Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297-301.
+% \cite{cooley_algorithm_1965}
+% \cite{cooley_algorithm_nodate}
 % 2. Hounsfield, G. N. (1973). Computerized transverse axial scanning (tomography): Part 1. Description of system. British Journal of Radiology, 46(552), 1016-1022.
+% \cite{hounsfield_computerized_1973}
 % 3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
+% \cite{lecun_deep_2015}
 % 4. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
+% \cite{hinton_improving_2012}
 
 \subsection{Current Applications of Image Processing in Industry}
 
@@ -58,48 +73,59 @@ Image processing, a critical component of computer vision, has become an indispe
 
 \subsubsection{Manufacturing and Quality Control} 
 
-In the manufacturing industry, image processing is pivotal for quality control and defect detection. Automated visual inspection systems utilize high-resolution cameras and sophisticated algorithms to detect defects in products at a speed and accuracy unattainable by human inspectors. For instance, in semiconductor manufacturing, image processing is used to inspect wafers for defects, ensuring that only flawless products proceed to the next production stage. This not only enhances product quality but also reduces waste and operational costs. A study by Zhang et al. (2020) highlights the use of convolutional neural networks (CNNs) in detecting surface defects in steel manufacturing, demonstrating significant improvements in detection accuracy and processing speed compared to traditional methods.
+In the manufacturing industry, image processing is pivotal for quality control and defect detection. Automated visual inspection systems utilize high-resolution cameras and sophisticated algorithms to detect defects in products at a speed and accuracy unattainable by human inspectors. For instance, in semiconductor manufacturing, image processing is used to inspect wafers for defects, ensuring that only flawless products proceed to the next production stage. This not only enhances product quality but also reduces waste and operational costs. A study by Zhang et al. (2023) \cite{zhang_efficient_2023} highlights the use of convolutional neural networks (CNNs) in detecting surface defects in steel manufacturing, demonstrating significant improvements in detection accuracy and processing speed compared to traditional methods.
 
 \subsubsection{Healthcare and Medical Imaging} 
 
-In healthcare, image processing is revolutionizing diagnostics and treatment planning. Techniques such as MRI, CT scans, and X-rays rely heavily on image processing to enhance image quality and extract meaningful information. For example, in radiology, image processing algorithms help in the early detection of diseases like cancer by improving the clarity and contrast of medical images, allowing for more accurate diagnoses. A research paper by Litjens et al. (2017) reviews the application of deep learning in medical imaging, showcasing its potential in improving diagnostic accuracy and efficiency, thus influencing patient outcomes positively.
+In healthcare, image processing is revolutionizing diagnostics and treatment planning. Techniques such as MRI, CT scans, and X-rays rely heavily on image processing to enhance image quality and extract meaningful information. For example, in radiology, image processing algorithms help in the early detection of diseases like cancer by improving the clarity and contrast of medical images, allowing for more accurate diagnoses. A research paper by Litjens et al. (2017) \cite{litjens_survey_2017} reviews the application of deep learning in medical imaging, showcasing its potential in improving diagnostic accuracy and efficiency, thus influencing patient outcomes positively.
 
 \subsubsection{Agriculture} 
 
-Precision agriculture benefits significantly from image processing, where it is used for crop monitoring, disease detection, and yield estimation. Drones equipped with multispectral cameras capture images of fields, which are then processed to assess plant health and detect stress factors such as pests or nutrient deficiencies. This enables farmers to make informed decisions, optimizing resource use and improving crop yields. A case study by Maimaitijiang et al. (2019) demonstrates the use of UAV-based hyperspectral imaging for monitoring crop growth, highlighting its effectiveness in enhancing agricultural productivity.
+Precision agriculture benefits significantly from image processing, where it is used for crop monitoring, disease detection, and yield estimation. Drones equipped with multispectral cameras capture images of fields, which are then processed to assess plant health and detect stress factors such as pests or nutrient deficiencies. This enables farmers to make informed decisions, optimizing resource use and improving crop yields. A case study by Maimaitijiang et al. (2020) \cite{maimaitijiang_soybean_2020} demonstrates the use of UAV-based hyperspectral imaging for monitoring crop growth, highlighting its effectiveness in enhancing agricultural productivity.
 
 \subsubsection{Automotive Industry} 
 
-In the automotive sector, image processing is integral to the development of autonomous vehicles. Advanced driver-assistance systems (ADAS) rely on image processing to interpret data from cameras and sensors, enabling features such as lane departure warnings, adaptive cruise control, and automatic parking. These systems enhance vehicle safety and pave the way for fully autonomous driving. A study by Janai et al. (2020) discusses the role of computer vision in autonomous vehicles, emphasizing the importance of real-time image processing in ensuring safe and efficient vehicle operation.
+In the automotive sector, image processing is integral to the development of autonomous vehicles. Advanced driver-assistance systems (ADAS) rely on image processing to interpret data from cameras and sensors, enabling features such as lane departure warnings, adaptive cruise control, and automatic parking. These systems enhance vehicle safety and pave the way for fully autonomous driving. A study by Janai et al. (2021) \cite{janai_computer_2021} discusses the role of computer vision in autonomous vehicles, emphasizing the importance of real-time image processing in ensuring safe and efficient vehicle operation.
 
 \subsubsection{Retail and E-commerce} 
 
-Retail and e-commerce industries leverage image processing for inventory management, customer analytics, and personalized marketing. In inventory management, image processing systems track stock levels and identify misplaced items, streamlining operations and reducing labor costs. In customer analytics, facial recognition and sentiment analysis provide insights into customer behavior and preferences, enabling personalized marketing strategies. A paper by Ren et al. (2019) explores the application of image processing in retail, highlighting its impact on enhancing customer experience and operational efficiency.
+Retail and e-commerce industries leverage image processing for inventory management, customer analytics, and personalized marketing. In inventory management, image processing systems track stock levels and identify misplaced items, streamlining operations and reducing labor costs. In customer analytics, facial recognition and sentiment analysis provide insights into customer behavior and preferences, enabling personalized marketing strategies. A paper by Ren et al. (2016) \cite{ren_faster_2016} explores the application of image processing in retail, highlighting its impact on enhancing customer experience and operational efficiency.
 
 % References
 
-% - Zhang, Y., Wang, S., & Liu, Y. (2020). Surface defect detection using convolutional neural networks. *Journal of Manufacturing Processes*, 49, 1-9.
+% - Zhang, Y., Wang, S., & Liu, Y. (2023). Surface defect detection using convolutional neural networks. *Journal of Manufacturing Processes*, 49, 1-9.
+% \cite{zhang_efficient_2023}
 % - Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. *Medical Image Analysis*, 42, 60-88.
+% \cite{litjens_survey_2017}
 % - Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., Fritschi, F. B., & Prasad, S. (2019). Soybean yield prediction from UAV using multimodal data fusion and deep learning. *Remote Sensing of Environment*, 233, 111-117.
-% - Janai, J., Güney, F., Behl, A., & Geiger, A. (2020). Computer vision for autonomous vehicles: Problems, datasets and state of the art. *Foundations and Trends® in Computer Graphics and Vision*, 12(1-3), 1-308.
-% - Ren, S., He, K., Girshick, R., & Sun, J. (2019). Faster R-CNN: Towards real-time object detection with region proposal networks. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 39(6), 1137-1149.
+% \cite{maimaitijiang_soybean_2020}
+% - Janai, J., Güney, F., Behl, A., & Geiger, A. (2021). Computer vision for autonomous vehicles: Problems, datasets and state of the art. *Foundations and Trends® in Computer Graphics and Vision*, 12(1-3), 1-308.
+% \cite{janai_computer_2021}
+% - Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: Towards real-time object detection with region proposal networks. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 39(6), 1137-1149.
+% \cite{ren_faster_2016}
 
 \subsection{The Strategic Importance of Image Processing Libraries}
 
 In the rapidly evolving landscape of industrial applications, the demand for efficient, adaptable, and scalable image processing libraries has become increasingly critical. These libraries serve as the backbone for a myriad of applications ranging from quality control in manufacturing to advanced robotics and autonomous systems. The benefits of employing such libraries are manifold, including reduced time-to-market, enhanced product quality, and cost efficiency, all of which are pivotal for maintaining competitive advantage in the industrial sector.
 
-Firstly, efficient image processing libraries significantly reduce the time-to-market for new products and technologies. In industries where innovation cycles are short and competition is fierce, the ability to quickly develop and deploy new solutions is crucial. Efficient libraries streamline the development process by providing pre-built, optimized functions that developers can readily integrate into their systems. This reduces the need for writing complex algorithms from scratch, thereby accelerating the development timeline. For instance, libraries like OpenCV and TensorFlow offer a wide array of tools and functions that can be easily adapted to specific industrial needs, allowing companies to focus on innovation rather than the intricacies of image processing (Bradski, 2000; Abadi et al., 2016).
+Firstly, efficient image processing libraries significantly reduce the time-to-market for new products and technologies. In industries where innovation cycles are short and competition is fierce, the ability to quickly develop and deploy new solutions is crucial. Efficient libraries streamline the development process by providing pre-built, optimized functions that developers can readily integrate into their systems. This reduces the need for writing complex algorithms from scratch, thereby accelerating the development timeline. For instance, libraries like OpenCV and TensorFlow offer a wide array of tools and functions that can be easily adapted to specific industrial needs, allowing companies to focus on innovation rather than the intricacies of image processing \cite{bradski_learning_2008}.
 
-Adaptability is another critical factor that underscores the importance of these libraries. Industrial environments are often dynamic, with varying requirements and conditions that necessitate flexible solutions. Scalable image processing libraries can be tailored to meet specific needs, whether it involves adjusting to different hardware configurations or integrating with other software systems. This adaptability ensures that companies can respond swiftly to changes in market demands or technological advancements without overhauling their entire system architecture. For example, the modular nature of libraries like Halide allows for easy customization and optimization for different hardware platforms, enhancing their applicability across diverse industrial scenarios (Ragan-Kelley et al., 2013).
+Adaptability is another critical factor that underscores the importance of these libraries. Industrial environments are often dynamic, with varying requirements and conditions that necessitate flexible solutions. Scalable image processing libraries can be tailored to meet specific needs, whether it involves adjusting to different hardware configurations or integrating with other software systems. This adaptability ensures that companies can respond swiftly to changes in market demands or technological advancements without overhauling their entire system architecture. For example, the modular nature of libraries like Halide allows for easy customization and optimization for different hardware platforms, enhancing their applicability across diverse industrial scenarios \cite{ragan-kelley_halide_2013}.
 
-Moreover, the use of scalable image processing libraries contributes to enhanced product quality. In industries such as automotive manufacturing or pharmaceuticals, precision and accuracy are paramount. Advanced image processing capabilities enable more rigorous quality control processes, ensuring that defects are detected and rectified early in the production cycle. This not only improves the quality of the final product but also minimizes waste and reduces the likelihood of costly recalls. Studies have shown that implementing robust image processing solutions can lead to significant improvements in defect detection rates and overall product reliability (Szeliski, 2010).
+Moreover, the use of scalable image processing libraries contributes to enhanced product quality. In industries such as automotive manufacturing or pharmaceuticals, precision and accuracy are paramount. Advanced image processing capabilities enable more rigorous quality control processes, ensuring that defects are detected and rectified early in the production cycle. This not only improves the quality of the final product but also minimizes waste and reduces the likelihood of costly recalls. Studies have shown that implementing robust image processing solutions can lead to significant improvements in defect detection rates and overall product reliability \cite{szeliski_image_2022}.
 
-Cost efficiency is another significant advantage offered by these libraries. By leveraging open-source or commercially available image processing tools, companies can reduce the costs associated with software development and maintenance. These libraries often come with extensive documentation and community support, which can further reduce the need for specialized training and technical support. Additionally, the ability to scale solutions according to demand means that companies can optimize their resource allocation, investing only in the capabilities they need at any given time. This scalability is particularly beneficial for small and medium-sized enterprises that may not have the resources to develop custom solutions from the ground up (Russell \& Norvig, 2016).
+Cost efficiency is another significant advantage offered by these libraries. By leveraging open-source or commercially available image processing tools, companies can reduce the costs associated with software development and maintenance. These libraries often come with extensive documentation and community support, which can further reduce the need for specialized training and technical support. Additionally, the ability to scale solutions according to demand means that companies can optimize their resource allocation, investing only in the capabilities they need at any given time. This scalability is particularly beneficial for small and medium-sized enterprises that may not have the resources to develop custom solutions from the ground up \cite{russell_artificial_2016}.
 
 % References
 
-% - Bradski, G. (2000). The OpenCV Library. *Dr. Dobb's Journal of Software Tools*.
+% - Bradski, G. (2008). The OpenCV Library. *Dr. Dobb's Journal of Software Tools*.
+% \cite{bradski_learning_2008}
 % - Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). TensorFlow: A System for Large-Scale Machine Learning. In *12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)* (pp. 265-283).
+% \cite{abadi_tensorflow_2016}
 % - Ragan-Kelley, J., Barnes, C., Adams, A., Paris, S., Durand, F., & Amarasinghe, S. (2013). Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines. *ACM SIGPLAN Notices*, 48(6), 519-530.
-% - Szeliski, R. (2010). *Computer Vision: Algorithms and Applications*. Springer Science & Business Media.
-% - Russell, S., & Norvig, P. (2016). *Artificial Intelligence: A Modern Approach*. Pearson.
\ No newline at end of file
+% \cite{ragan-kelley_halide_2013}
+% - Szeliski, R. (2022). *Computer Vision: Algorithms and Applications*. Springer Science & Business Media.
+% \cite{szeliski_image_2022}
+% \cite{szeliski_introduction_2022}
+% - Russell, S., & Norvig, P. (2016). *Artificial Intelligence: A Modern Approach*. Pearson.
+% \cite{russell_artificial_2016}
\ No newline at end of file
diff --git a/sections/Chapter-1-sections/Related-Work.tex b/sections/Chapter-1-sections/Related-Work.tex
index a6207b9..7c81df2 100644
--- a/sections/Chapter-1-sections/Related-Work.tex
+++ b/sections/Chapter-1-sections/Related-Work.tex
@@ -2,11 +2,11 @@
 
 The evaluation of image processing libraries, particularly for industrial applications, has attracted significant research interest over the past decades. Broadly, the field encompasses automated image analysis and computer vision systems designed to handle tasks such as quality control, defect detection, and high-resolution image enhancement. The foundational research in Automated Image Processing has evolved from early, often ad hoc, implementations to sophisticated frameworks that leverage hardware acceleration and advanced algorithms. Early surveys, such as Kulpa’s (1981) \cite{kulpa_universal_1981} seminal review of digital image processing systems in Europe, laid the groundwork for understanding the challenges of standardization and performance evaluation in these systems.
 
-In recent years, the convergence of hardware acceleration and image analysis has been a recurring theme. Sahebi et al. (2023) demonstrate how distributed processing on FPGAs can dramatically enhance computational efficiency—a principle equally applicable to industrial image processing where real-time performance is critical. Similarly, Ma et al. (2024) \cite{ma_new_2024} contribute to the field by presenting an image quality database specifically tailored for industrial processes. Their work emphasizes the importance of aligning objective metrics with human perception in quality assessments, a concern that resonates throughout subsequent research in the area.
+In recent years, the convergence of hardware acceleration and image analysis has been a recurring theme. Sahebi et al. (2023) \cite{sahebi_distributed_2023} demonstrate how distributed processing on FPGAs can dramatically enhance computational efficiency—a principle equally applicable to industrial image processing where real-time performance is critical. Similarly, Ma et al. (2024) \cite{ma_new_2024} contribute to the field by presenting an image quality database specifically tailored for industrial processes. Their work emphasizes the importance of aligning objective metrics with human perception in quality assessments, a concern that resonates throughout subsequent research in the area.
 
 Chisholm et al. (2020) \cite{chisholm_fpga-based_2020} and Ferreira et al. (2024) \cite{ferreira_generic_2024} extend these discussions by focusing on the implementation of real-time image processing systems using FPGAs. Chisholm illustrate a real-time crack detection system employing particle filters, highlighting the challenges of meeting stringent timing constraints in industrial settings. Ferreira, on the other hand, propose a generic FPGA-based pre-processing library, emphasizing strategies to minimize memory overhead and improve processing speed. These studies underscore the significant role of hardware acceleration in modern image processing pipelines, setting the stage for more nuanced comparative evaluations.
 
-A critical aspect of the research is the comparative analysis of different image processing libraries. Lai et al. (2001) provide an in-depth review of several libraries, contrasting hardware-specific optimizations with generic, portable solutions. Their work not only identifies the strengths and weaknesses inherent in different design philosophies but also serves as a benchmark against which later approaches can be compared. Kulpa’s early survey (1981) \cite{kulpa_universal_1981} remains an important historical reference, offering insights into the evolution of image processing systems and highlighting persistent issues such as limited standardization and documentation.
+A critical aspect of the research is the comparative analysis of different image processing libraries. Lai et al. (2001) \cite{lai_image_2001} provide an in-depth review of several libraries, contrasting hardware-specific optimizations with generic, portable solutions. Their work not only identifies the strengths and weaknesses inherent in different design philosophies but also serves as a benchmark against which later approaches can be compared. Kulpa’s early survey (1981) \cite{kulpa_universal_1981} remains an important historical reference, offering insights into the evolution of image processing systems and highlighting persistent issues such as limited standardization and documentation.
 
 Pérez et al. (2014) \cite{perez_super-resolution_2014} contribute by investigating super-resolution techniques for plenoptic cameras via FPGA-based implementations, demonstrating that hardware acceleration can significantly improve both processing speed and image quality. Meanwhile, Rao’s (2023) \cite{rao_comparative_2023} comparative analysis of deep learning frameworks extends the conversation by incorporating performance metrics, documentation quality, and community support. This approach is particularly valuable as it parallels the metrics used to evaluate traditional image processing libraries, thereby bridging the gap between classical image processing and modern deep learning paradigms.
 
diff --git a/sections/Chapter-1-sections/Relevance.tex b/sections/Chapter-1-sections/Relevance.tex
index dbc24df..de18aa7 100644
--- a/sections/Chapter-1-sections/Relevance.tex
+++ b/sections/Chapter-1-sections/Relevance.tex
@@ -1,8 +1,8 @@
 \section{Relevance of Image Processing Libraries in Industrial Contexts}
 
-In the rapidly evolving landscape of industrial applications, the evaluation of image processing libraries has emerged as a critical area of focus, particularly for companies like Dassault Systèmes, a leader in 3D design, 3D digital mock-up, and product lifecycle management (PLM) software. The relevance of this evaluation extends beyond academic curiosity, delving into the practical implications that these technologies hold for enhancing operational efficiency, product quality, and innovation in industrial settings. Image processing libraries serve as the backbone for a myriad of applications, from quality control and predictive maintenance to advanced simulations and virtual prototyping, all of which are integral to the operations at Dassault Systèmes.
+In the rapidly evolving landscape of industrial applications, the evaluation of image processing libraries has emerged as a critical area of focus, particularly for companies like Dassault Systèmes, a leader in 3D design, 3D digital mock-up, and product lifecycle management (PLM) software. The relevance of this evaluation extends beyond academic curiosity, exploring the practical implications that these technologies hold for enhancing operational efficiency, product quality, and innovation in industrial settings. Image processing libraries serve as the backbone for a myriad of applications, from quality control and predictive maintenance to advanced simulations and virtual prototyping, all of which are integral to the operations at Dassault Systèmes.
 
-The industrial sector is increasingly reliant on sophisticated image processing techniques to automate and optimize processes, reduce human error, and improve decision-making capabilities. For instance, in quality control, image processing can detect defects in products with a precision that surpasses human capabilities, thereby ensuring higher standards of quality and reducing waste (Gonzalez \& Woods, 2018). Furthermore, in the realm of predictive maintenance, these libraries enable the analysis of visual data to predict equipment failures before they occur, thus minimizing downtime and maintenance costs (Szeliski, 2010).
+The industrial sector is increasingly reliant on sophisticated image processing techniques to automate and optimize processes, reduce human error, and improve decision-making capabilities. For instance, in quality control, image processing can detect defects in products with a precision that surpasses human capabilities, thereby ensuring higher standards of quality and reducing waste (Gonzalez \& Woods, 2008). Furthermore, in the realm of predictive maintenance, these libraries enable the analysis of visual data to predict equipment failures before they occur, thus minimizing downtime and maintenance costs (Szeliski, 2010).
 
 For Dassault Systèmes, whose software solutions are pivotal in designing and managing complex industrial systems, the choice of image processing libraries can significantly impact the performance and capabilities of their products. By evaluating and selecting the most efficient and robust libraries, Dassault Systèmes can enhance the functionality of their software, offering clients more powerful tools for simulation and analysis. This not only strengthens their competitive edge but also aligns with the broader industry trend towards digital transformation and smart manufacturing (Chui et al., 2018).
 
@@ -10,54 +10,40 @@ Moreover, the integration of advanced image processing capabilities into Dassaul
 
 % References
 
-% - Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing. Pearson.
+% - Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing. Pearson.
+\cite{gonzalez_digital_2008-1}
 % - Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.
+\cite{szeliski_image_2022}
 % - Chui, M., Manyika, J., & Miremadi, M. (2018). The Future of Work in America: People and Places, Today and Tomorrow. McKinsey Global Institute.
 
-\subsection{Ubiquity of Image Processing Requirements}
-
-Image processing has evolved into a cornerstone technology across various industries, significantly impacting fields such as manufacturing, healthcare, security, and entertainment. Its ability to enhance, analyze, and manipulate images has led to innovations that streamline operations, improve accuracy, and enable new capabilities. Understanding the capabilities of different image processing libraries is crucial for optimizing performance and resource management, especially in environments with varying computational constraints.
-
-In manufacturing, image processing is pivotal for quality control and automation. Techniques such as edge detection, pattern recognition, and object classification are employed to inspect products for defects, ensuring high standards and reducing waste. For instance, in semiconductor manufacturing, image processing algorithms are used to detect microscopic defects on wafers, which is critical for maintaining the integrity of electronic components (Zhou et al., 2019). The ability to process images in real-time allows for immediate feedback and adjustments in the production line, enhancing efficiency and reducing downtime.
-
-Healthcare has also seen transformative changes due to image processing. Medical imaging technologies, such as MRI, CT scans, and X-rays, rely heavily on image processing to enhance image quality and assist in diagnosis. Advanced algorithms can detect anomalies in medical images, aiding radiologists in identifying diseases at earlier stages. For example, deep learning-based image processing techniques have been used to improve the accuracy of breast cancer detection in mammograms, significantly impacting patient outcomes (Litjens et al., 2017).
-
-The choice of image processing libraries is critical in both high-performance and resource-constrained environments. Libraries such as OpenCV, TensorFlow, and PyTorch offer a range of functionalities that cater to different needs. OpenCV, known for its speed and efficiency, is often used in real-time applications where quick processing is essential. TensorFlow and PyTorch, with their robust support for deep learning, are preferred for applications requiring complex neural network models. Understanding the strengths and limitations of these libraries allows developers to select the most appropriate tools for their specific use cases, balancing performance with resource availability.
-
-In resource-constrained environments, such as mobile devices or embedded systems, optimizing image processing tasks is crucial. Lightweight libraries and techniques, such as quantization and model pruning, can reduce computational load and power consumption without significantly compromising accuracy. This is particularly important in applications like mobile health monitoring, where devices must process images efficiently to provide timely feedback to users (Howard et al., 2017).
-
-% References
-
-% - Zhou, Y., Wang, Y., & Zhang, J. (2019). Defect detection in semiconductor manufacturing using image processing techniques. *Journal of Manufacturing Processes*, 45, 123-130.
-% - Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. *Medical Image Analysis*, 42, 60-88.
-% - Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. *arXiv preprint arXiv:1704.04861*.
-
 \subsection{Hardware Considerations in Image Processing}
 
 The use of image processing libraries across different hardware platforms, such as powerful servers and embedded systems, presents a range of implications that are crucial for developers and engineers to consider. These implications are primarily centered around performance metrics like speed, memory usage, and power consumption, which significantly influence the choice of libraries for specific applications.
 
-**Speed** is a critical performance metric in image processing, especially in applications requiring real-time processing, such as autonomous vehicles, surveillance systems, and augmented reality. On powerful servers, libraries like OpenCV and TensorFlow can leverage high computational power and parallel processing capabilities to deliver fast processing speeds. These libraries are optimized to take advantage of multi-core CPUs and GPUs, which are abundant in server environments. In contrast, embedded systems, which often have limited processing power, may require lightweight libraries such as CImg or SimpleCV that are optimized for speed on less powerful hardware. The choice of library, therefore, depends on the ability to meet the application's speed requirements within the constraints of the hardware.
+\textbf{Speed} is a critical performance metric in image processing, especially in applications requiring real-time processing, such as autonomous vehicles, surveillance systems, and augmented reality. On powerful servers, libraries like OpenCV and TensorFlow can leverage high computational power and parallel processing capabilities to deliver fast processing speeds. These libraries are optimized to take advantage of multi-core CPUs and GPUs, which are abundant in server environments. In contrast, embedded systems, which often have limited processing power, may require lightweight libraries such as CImg or SimpleCV that are optimized for speed on less powerful hardware. The choice of library, therefore, depends on the ability to meet the application's speed requirements within the constraints of the hardware.
 
-**Memory usage** is another crucial factor, particularly in embedded systems where memory resources are limited. Libraries that are memory-efficient are preferred in such environments to ensure that the system can handle image processing tasks without exhausting available memory. For instance, libraries like Halide are designed to optimize memory usage through techniques such as memory tiling and scheduling, making them suitable for memory-constrained environments. On the other hand, powerful servers with abundant memory resources can afford to use more memory-intensive libraries if they offer other advantages, such as ease of use or additional features.
+\textbf{Memory usage} is another crucial factor, particularly in embedded systems where memory resources are limited. Libraries that are memory-efficient are preferred in such environments to ensure that the system can handle image processing tasks without exhausting available memory. For instance, libraries like Halide are designed to optimize memory usage through techniques such as memory tiling and scheduling, making them suitable for memory-constrained environments. On the other hand, powerful servers with abundant memory resources can afford to use more memory-intensive libraries if they offer other advantages, such as ease of use or additional features.
 
-**Power consumption** is a significant consideration, especially in battery-powered embedded systems. High power consumption can lead to reduced battery life, which is undesirable in applications like mobile devices and remote sensors. Libraries that are optimized for low power consumption, such as those that minimize CPU usage or leverage specialized hardware accelerators, are preferred in these scenarios. For example, the use of hardware-specific libraries that utilize Digital Signal Processors (DSPs) or Graphics Processing Units (GPUs) can significantly reduce power consumption while maintaining performance.
+\textbf{Power consumption} is a significant consideration, especially in battery-powered embedded systems. High power consumption can lead to reduced battery life, which is undesirable in applications like mobile devices and remote sensors. Libraries that are optimized for low power consumption, such as those that minimize CPU usage or leverage specialized hardware accelerators, are preferred in these scenarios. For example, the use of hardware-specific libraries that utilize Digital Signal Processors (DSPs) or Graphics Processing Units (GPUs) can significantly reduce power consumption while maintaining performance.
 
 Research has shown that hardware constraints are a significant factor in choosing image processing solutions. For instance, a study by [Smith et al. (2020)] demonstrated that the choice of image processing libraries for a drone-based surveillance system was heavily influenced by the need to balance processing speed and power consumption, leading to the selection of a library that could efficiently utilize the drone's onboard GPU. Similarly, [Jones and Patel (2019)] highlighted the importance of memory efficiency in selecting image processing libraries for a wearable health monitoring device, where limited memory resources necessitated the use of a highly optimized library.
 
 % References
 
 % - Smith, J., et al. (2020). "Optimizing Image Processing for Drone-Based Surveillance Systems." Journal of Embedded Systems, 15(3), 45-60.
+
 % - Jones, A., & Patel, R. (2019). "Memory-Efficient Image Processing for Wearable Health Monitoring Devices." International Journal of Computer Vision, 112(2), 123-137.
 
+
 \subsection{Performance Metrics and Their Impact on Use Cases}
 
 Performance metrics such as latency, throughput, and resource efficiency are critical in determining the practical applications of image processing libraries. These metrics directly influence the feasibility, scalability, and cost-effectiveness of deploying image processing solutions across various industries, including those served by companies like Dassault Systèmes.
 
-**Latency** refers to the time delay between the input of an image and the completion of its processing. In real-time applications, such as autonomous vehicles or live video surveillance, low latency is crucial. For instance, in autonomous driving, the system must process images from cameras in real-time to make immediate decisions. High latency could lead to delayed responses, potentially causing accidents. Research has shown that optimizing algorithms for lower latency can significantly enhance the performance of real-time systems (Zhang et al., 2020).
+\textbf{Latency} refers to the time delay between the input of an image and the completion of its processing. In real-time applications, such as autonomous vehicles or live video surveillance, low latency is crucial. For instance, in autonomous driving, the system must process images from cameras in real-time to make immediate decisions. High latency could lead to delayed responses, potentially causing accidents. Research has shown that optimizing algorithms for lower latency can significantly enhance the performance of real-time systems (Zhang et al., 2020).
 
-**Throughput** is the rate at which images are processed over a given period. High throughput is essential in applications like medical imaging, where large volumes of data need to be processed quickly to assist in diagnostics. For example, in radiology, the ability to process and analyze thousands of images rapidly can improve diagnostic accuracy and patient throughput in hospitals. Studies have demonstrated that optimizing image processing libraries for higher throughput can lead to more efficient healthcare delivery (Smith et al., 2019).
+\textbf{Throughput} is the rate at which images are processed over a given period. High throughput is essential in applications like medical imaging, where large volumes of data need to be processed quickly to assist in diagnostics. For example, in radiology, the ability to process and analyze thousands of images rapidly can improve diagnostic accuracy and patient throughput in hospitals. Studies have demonstrated that optimizing image processing libraries for higher throughput can lead to more efficient healthcare delivery (Smith et al., 2019).
 
-**Resource Efficiency** involves the optimal use of computational resources, such as CPU, GPU, and memory. Efficient resource utilization is vital for reducing operational costs and energy consumption, particularly in large-scale deployments. In industries like aerospace, where Dassault Systèmes operates, resource efficiency can lead to significant cost savings. For instance, in the design and simulation of aircraft components, efficient image processing can reduce the computational load, leading to faster design iterations and reduced time-to-market. Research indicates that resource-efficient algorithms can lead to substantial improvements in operational efficiency (Lee et al., 2021).
+\textbf{Resource Efficiency} involves the optimal use of computational resources, such as CPU, GPU, and memory. Efficient resource utilization is vital for reducing operational costs and energy consumption, particularly in large-scale deployments. In industries like aerospace, where Dassault Systèmes operates, resource efficiency can lead to significant cost savings. For instance, in the design and simulation of aircraft components, efficient image processing can reduce the computational load, leading to faster design iterations and reduced time-to-market. Research indicates that resource-efficient algorithms can lead to substantial improvements in operational efficiency (Lee et al., 2021).
 
 In the context of Dassault Systèmes, these performance metrics are particularly relevant. The company provides 3D design, 3D digital mock-up, and product lifecycle management (PLM) software. In these applications, image processing is used extensively for rendering 3D models, simulating real-world scenarios, and visualizing complex data. For example, in the automotive industry, Dassault Systèmes' solutions are used to design and test vehicles virtually. Here, low latency and high throughput are crucial for real-time simulations and analyses, while resource efficiency ensures that these processes are cost-effective and sustainable.
 
@@ -67,25 +53,4 @@ Moreover, Dassault Systèmes' involvement in smart city projects requires effici
 
 % - Zhang, Y., Wang, X., & Li, J. (2020). Real-time image processing in autonomous vehicles: A survey. *Journal of Real-Time Image Processing*, 17(3), 567-589.
 % - Smith, A., Jones, B., & Patel, C. (2019). High-throughput medical imaging: Challenges and solutions. *Medical Image Analysis*, 58, 101-112.
-% - Lee, H., Kim, S., & Park, J. (2021). Resource-efficient algorithms for large-scale image processing. *IEEE Transactions on Image Processing*, 30, 1234-1245.
-
-\subsection{Specific Use Cases at Dassault Systems}
-
-Dassault Systèmes, a leader in 3D design and engineering software, integrates image processing libraries into its products to enhance functionality and address unique challenges in product design, simulation, and quality assurance. While specific proprietary details are confidential, general industry practices provide insight into how these integrations can be beneficial.
-
-In product design, image processing libraries are crucial for converting real-world images into digital models. This process, known as photogrammetry, allows designers to create accurate 3D models from photographs. By integrating image processing libraries, Dassault Systèmes' software can automate the conversion of 2D images into 3D models, significantly reducing the time and effort required for manual modeling. This capability is particularly useful in industries such as automotive and aerospace, where precision and accuracy are paramount (Remondino \& El-Hakim, 2006).
-
-In simulation, image processing libraries enhance the visualization and analysis of complex data. For instance, in finite element analysis (FEA), these libraries can process and visualize stress distribution images, helping engineers identify potential failure points in a design. By providing clear, detailed visualizations, image processing tools enable engineers to make informed decisions about material selection and structural modifications, ultimately improving product safety and performance (Bathe, 2006).
-
-Quality assurance is another area where image processing libraries play a vital role. Automated inspection systems use these libraries to analyze images of manufactured parts, identifying defects such as cracks, misalignments, or surface irregularities. By integrating image processing capabilities, Dassault Systèmes' solutions can offer real-time quality control, reducing the need for manual inspections and minimizing the risk of defective products reaching the market. This approach is widely used in manufacturing industries to ensure high standards of product quality and consistency (Szeliski, 2010).
-
-Furthermore, image processing libraries facilitate the integration of augmented reality (AR) and virtual reality (VR) technologies into Dassault Systèmes' products. These technologies rely heavily on image processing to overlay digital information onto the real world or create immersive virtual environments. In product design and simulation, AR and VR can provide interactive, 3D visualizations of products, allowing designers and engineers to explore and refine their creations in a virtual space before physical prototypes are built (Azuma, 1997).
-
-In conclusion, the integration of image processing libraries into Dassault Systèmes' products enhances functionality across various stages of product development. By automating model creation, improving data visualization, ensuring quality assurance, and enabling AR/VR applications, these libraries address unique challenges in design, simulation, and manufacturing. While specific implementations within Dassault Systèmes remain confidential, the general industry applications underscore the transformative impact of image processing technologies in engineering and design.
-
-% References
-
-% - Remondino, F., & El-Hakim, S. (2006). Image-based 3D modelling: A review. *The Photogrammetric Record*, 21(115), 269-291.
-% - Bathe, K. J. (2006). *Finite Element Procedures*. Prentice Hall.
-% - Szeliski, R. (2010). *Computer Vision: Algorithms and Applications*. Springer.
-% - Azuma, R. T. (1997). A survey of augmented reality. *Presence: Teleoperators & Virtual Environments*, 6(4), 355-385.
\ No newline at end of file
+% - Lee, H., Kim, S., & Park, J. (2021). Resource-efficient algorithms for large-scale image processing. *IEEE Transactions on Image Processing*, 30, 1234-1245.
\ No newline at end of file
diff --git a/sources/references.bib b/sources/references.bib
index 045f0d8..5c6386d 100644
--- a/sources/references.bib
+++ b/sources/references.bib
@@ -297,3 +297,362 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	pages = {337--342},
 	file = {Full Text PDF:C\:\\Users\\SFI19\\Zotero\\storage\\25G2NS2A\\Rao - 2023 - A Comparative Analysis of Deep Learning Frameworks and Libraries.pdf:application/pdf},
 }
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+
+@book{goodfellow_deep_2016,
+	title = {Deep {Learning}},
+	isbn = {978-0-262-03561-3},
+	abstract = {An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.},
+	language = {en},
+	publisher = {MIT Press},
+	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
+	month = nov,
+	year = {2016},
+	note = {Google-Books-ID: Np9SDQAAQBAJ},
+	keywords = {Computers / Artificial Intelligence / General, Computers / Computer Science, Computers / Data Science / Machine Learning},
+}
+
+@book{gonzalez_digital_2008,
+	title = {Digital image processing},
+	isbn = {978-0-13-168728-8 978-0-13-505267-9},
+	url = {http://archive.org/details/digitalimageproc0003gonz},
+	abstract = {xxii, 954 pages : 25 cm; Completely self-contained-and heavily illustrated-this introduction to basic concepts and methodologies for digital image processing is written at a level that truly is suitable for seniors and first-year graduate students in almost any technical discipline. The leading textbook in its field for more than twenty years, it continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing-e.g., image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, image description, and the fundamentals of object recognition. It focuses on material that is fundamental and has a broad scope of application; Includes bibliographical references (pages 915-942) and index; Introduction -- Digital image fundamentals -- Intensity transformations and spatial filtering -- Filtering the frequency domain -- Image restoration and reconstruction -- Color image processing -- Wavelets and multiresolution processing -- Image compression -- Morphological image processing -- Image segmentation -- Representation and description -- Object recongnition},
+	language = {eng},
+	urldate = {2025-02-09},
+	publisher = {Upper Saddle River, N.J. : Prentice Hall},
+	author = {Gonzalez, Rafael C.},
+	collaborator = {{Internet Archive}},
+	year = {2008},
+	keywords = {Image processing -- Digital techniques},
+}
+
+@book{gonzalez_digital_2008-1,
+	title = {Digital {Image} {Processing}},
+	isbn = {978-0-13-168728-8},
+	abstract = {For courses in Image Processing and Computer Vision.   Completely self-contained--and heavily illustrated--this introduction to basic concepts and methodologies for digital image processing is written at a level that truly is suitable for seniors and first-year graduate students in almost any technical discipline. The leading textbook in its field for more than twenty years, it continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing--e.g., image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, image description, and the fundamentals of object recognition. It focuses on material that is fundamental and has a broad scope of application.},
+	language = {en},
+	publisher = {Prentice Hall},
+	author = {Gonzalez, Rafael C. and Woods, Richard Eugene},
+	year = {2008},
+	note = {Google-Books-ID: 8uGOnjRGEzoC},
+	keywords = {Computers / Image Processing, Computers / Optical Data Processing, Technology \& Engineering / Imaging Systems, Technology \& Engineering / Signals \& Signal Processing},
+}
+
+@book{jain_fundamentals_1989,
+	title = {Fundamentals of {Digital} {Image} {Processing}},
+	isbn = {978-0-13-336165-0},
+	abstract = {Presents a thorough overview of the major topics of digital image processing, beginning with the basic mathematical tools needed for the subject. Includes a comprehensive chapter on stochastic models for digital image processing.   Covers aspects of image representation including luminance, color, spatial and temporal properties of vision, and digitization. Explores various image processing techniques. Discusses algorithm development (software/firmware) for image transforms, enhancement, reconstruction, and image coding.},
+	language = {en},
+	publisher = {Prentice Hall},
+	author = {Jain, Anil K.},
+	year = {1989},
+	note = {Google-Books-ID: GANSAAAAMAAJ},
+	keywords = {Computers / Image Processing, Computers / Optical Data Processing, Technology \& Engineering / Imaging Systems, Technology \& Engineering / Signals \& Signal Processing, Science / Physics / Optics \& Light, Technology \& Engineering / Electrical, Technology \& Engineering / Telecommunications},
+}
+
+@book{jain_fundamentals_1989-1,
+	title = {Fundamentals of digital image processing},
+	isbn = {978-0-13-336165-0},
+	url = {http://archive.org/details/fundamentalsofdi0000jain},
+	abstract = {xxi, 569 p. : 24 cm; Includes bibliographical references and index},
+	language = {eng},
+	urldate = {2025-02-09},
+	publisher = {Englewood Cliffs, NJ : Prentice Hall},
+	author = {Jain, Anil K.},
+	collaborator = {{Internet Archive}},
+	year = {1989},
+	keywords = {Image processing -- Digital techniques},
+}
+
+@book{russ_image_2016,
+	title = {The {Image} {Processing} {Handbook}},
+	isbn = {978-1-4398-4063-4},
+	abstract = {Whether obtained by microscopes, space probes, or the human eye, the same basic tools can be applied to acquire, process, and analyze the data contained in images. Ideal for self study, The Image Processing Handbook, Sixth Edition, first published in 1992, raises the bar once again as the gold-standard reference on this subject. Using extensive new illustrations and diagrams, it offers a logically organized exploration of the important relationship between 2D images and the 3D structures they reveal.  Provides Hundreds of Visual Examples in FULL COLOR! The author focuses on helping readers visualize and compare processing and measurement operations and how they are typically combined in fields ranging from microscopy and astronomy to real-world scientific, industrial, and forensic applications. Presenting methods in the order in which they would be applied in a typical workflow—from acquisition to interpretation—this book compares a wide range of algorithms used to:    Improve the appearance, printing, and transmission of an image Prepare images for measurement of the features and structures they reveal Isolate objects and structures, and measure their size, shape, color, and position Correct defects and deal with limitations in images Enhance visual content and interpretation of details This handbook avoids dense mathematics, instead using new practical examples that better convey essential principles of image processing. This approach is more useful to develop readers’ grasp of how and why to apply processing techniques and ultimately process the mathematical foundations behind them.  Much more than just an arbitrary collection of algorithms, this is the rare book that goes beyond mere image improvement, presenting a wide range of powerful example images that illustrate techniques involved in color processing and enhancement. Applying his 50-year experience as a scientist, educator, and industrial consultant, John Russ offers the benefit of his image processing expertise for fields ranging from astronomy and biomedical research to food science and forensics. His valuable insights and guidance continue to make this handbook a must-have reference.},
+	language = {en},
+	publisher = {CRC Press},
+	author = {Russ, John C.},
+	month = apr,
+	year = {2016},
+	note = {Google-Books-ID: gxXXRJWfEsoC},
+	keywords = {Computers / Optical Data Processing, Technology \& Engineering / Imaging Systems, Computers / General, Medical / Biotechnology, Technology \& Engineering / Biomedical},
+}
+
+@book{bradski_learning_2008,
+	title = {Learning {OpenCV} : computer vision with the {OpenCV} library},
+	isbn = {978-0-596-51613-0},
+	shorttitle = {Learning {OpenCV}},
+	url = {http://archive.org/details/learningopencvco0000brad},
+	abstract = {xvii, 555 pages : 24 cm; Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time; Includes bibliographical references (pages 527-541) and index; Overview -- Introduction to OpenCV -- Getting to know OpenCV -- HighGUI -- Image processing -- Image transforms -- Histograms and matching -- Contours -- Image parts and segmentation -- Tracking and motion -- Camera models and calibration -- Projection and 3D vision -- Machine learning -- OpenCV's future},
+	language = {eng},
+	urldate = {2025-03-23},
+	publisher = {Sebastopol, CA : O'Reilly},
+	author = {Bradski, Gary R.},
+	collaborator = {{Internet Archive}},
+	year = {2008},
+	keywords = {OpenCV},
+}
+
+@incollection{szeliski_image_2022,
+	address = {Cham},
+	title = {Image {Processing}},
+	isbn = {978-3-030-34372-9},
+	url = {https://doi.org/10.1007/978-3-030-34372-9_3},
+	abstract = {Now that we have seen how images are formed through the interaction of 3D scene elements, lighting, and camera optics and sensors, let us look at the first stage in most computer vision algorithms, namely the use of image processing to preprocess the image and convert it into a form suitable for further analysis. Examples of such operations include exposure correction and color balancing, reducing image noise, increasing sharpness, or straightening the image by rotating it.},
+	language = {en},
+	urldate = {2025-03-23},
+	booktitle = {Computer {Vision}: {Algorithms} and {Applications}},
+	publisher = {Springer International Publishing},
+	author = {Szeliski, Richard},
+	editor = {Szeliski, Richard},
+	year = {2022},
+	doi = {10.1007/978-3-030-34372-9_3},
+	pages = {85--151},
+	file = {Full Text PDF:C\:\\Users\\SFI19\\Zotero\\storage\\4E9CTSBC\\Szeliski - 2022 - Image Processing.pdf:application/pdf},
+}
+
+@incollection{szeliski_introduction_2022,
+	address = {Cham},
+	title = {Introduction},
+	isbn = {978-3-030-34372-9},
+	url = {https://doi.org/10.1007/978-3-030-34372-9_1},
+	abstract = {As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you.},
+	language = {en},
+	urldate = {2025-03-23},
+	booktitle = {Computer {Vision}: {Algorithms} and {Applications}},
+	publisher = {Springer International Publishing},
+	author = {Szeliski, Richard},
+	editor = {Szeliski, Richard},
+	year = {2022},
+	doi = {10.1007/978-3-030-34372-9_1},
+	pages = {1--26},
+	file = {Full Text PDF:C\:\\Users\\SFI19\\Zotero\\storage\\AWW7TKMC\\Szeliski - 2022 - Introduction.pdf:application/pdf},
+}
+
+@misc{noauthor_computer_2010,
+	title = {Computer {Vision}: {Algorithms} and {Applications}},
+	shorttitle = {Computer {Vision}},
+	url = {https://scispace.com/papers/computer-vision-algorithms-and-applications-25dn6wu83j},
+	abstract = {Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.},
+	language = {en},
+	urldate = {2025-03-23},
+	journal = {SciSpace - Paper},
+	month = sep,
+	year = {2010},
+	file = {Full Text PDF:C\:\\Users\\SFI19\\Zotero\\storage\\BG7IZ622\\2010 - Computer Vision Algorithms and Applications.pdf:application/pdf},
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+@article{cooley_algorithm_nodate,
+	title = {An {Algorithm} for the {Machine} {Calculation} of {Complex} {Fourier} {Series}},
+	language = {en},
+	author = {Cooley, James W and Tukey, John W},
+	file = {PDF:C\:\\Users\\SFI19\\Zotero\\storage\\MCLKRX9H\\Cooley and Tukey - An Algorithm for the Machine Calculation of Complex Fourier Series.pdf:application/pdf},
+}
+
+@article{cooley_algorithm_1965,
+	title = {An {Algorithm} for the {Machine} {Calculation} of {Complex} {Fourier} {Series}},
+	volume = {19},
+	issn = {0025-5718},
+	url = {https://www.jstor.org/stable/2003354},
+	doi = {10.2307/2003354},
+	number = {90},
+	urldate = {2025-03-23},
+	journal = {Mathematics of Computation},
+	author = {Cooley, James W. and Tukey, John W.},
+	year = {1965},
+	note = {Publisher: American Mathematical Society},
+	pages = {297--301},
+	file = {Full Text:C\:\\Users\\SFI19\\Zotero\\storage\\DJ3PD27D\\Cooley and Tukey - 1965 - An Algorithm for the Machine Calculation of Complex Fourier Series.pdf:application/pdf},
+}
+
+@article{hounsfield_computerized_1973,
+	title = {Computerized transverse axial scanning (tomography): {Part} 1. {Description} of system},
+	volume = {46},
+	issn = {0007-1285},
+	shorttitle = {Computerized transverse axial scanning (tomography)},
+	url = {https://doi.org/10.1259/0007-1285-46-552-1016},
+	doi = {10.1259/0007-1285-46-552-1016},
+	abstract = {This article describes a technique in which X-ray transmission readings are taken through the head at a multitude of angles: from these data, absorption values of the material contained within the head are calculated on a computer and presented as a series of pictures of slices of the cranium. The system is approximately 100 times more sensitive than conventional X-ray systems to such an extent that variations in soft tissues of nearly similar density can be displayed.},
+	number = {552},
+	urldate = {2025-03-23},
+	journal = {British Journal of Radiology},
+	author = {Hounsfield, G. N.},
+	month = dec,
+	year = {1973},
+	pages = {1016--1022},
+	file = {Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\Q676YM6Q\\7306149.html:text/html},
+}
+
+@article{lecun_deep_2015,
+	title = {Deep learning},
+	volume = {521},
+	issn = {1476-4687},
+	doi = {10.1038/nature14539},
+	abstract = {Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
+	number = {7553},
+	journal = {Nature},
+	author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
+	year = {2015},
+	note = {Place: United Kingdom
+Publisher: Nature Publishing Group},
+	keywords = {Algorithms, Computational Modeling, Machine Learning, Object Recognition},
+	pages = {436--444},
+}
+
+@misc{hinton_improving_2012,
+	title = {Improving neural networks by preventing co-adaptation of feature detectors},
+	url = {http://arxiv.org/abs/1207.0580},
+	doi = {10.48550/arXiv.1207.0580},
+	abstract = {When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition.},
+	urldate = {2025-03-23},
+	publisher = {arXiv},
+	author = {Hinton, Geoffrey E. and Srivastava, Nitish and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan R.},
+	month = jul,
+	year = {2012},
+	note = {arXiv:1207.0580 [cs]},
+	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
+	file = {Preprint PDF:C\:\\Users\\SFI19\\Zotero\\storage\\25BYMHFC\\Hinton et al. - 2012 - Improving neural networks by preventing co-adaptation of feature detectors.pdf:application/pdf;Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\HIDVZ7NV\\1207.html:text/html},
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+@article{zhang_efficient_2023,
+	title = {An efficient lightweight convolutional neural network for industrial surface defect detection},
+	volume = {56},
+	issn = {1573-7462},
+	url = {https://doi.org/10.1007/s10462-023-10438-y},
+	doi = {10.1007/s10462-023-10438-y},
+	abstract = {Since surface defect detection is significant to ensure the utility, integrality, and security of productions, and it has become a key issue to control the quality of industrial products, which arouses interests of researchers. However, deploying deep convolutional neural networks (DCNNs) on embedded devices is very difficult due to limited storage space and computational resources. In this paper, an efficient lightweight convolutional neural network (CNN) model is designed for surface defect detection of industrial productions in the perspective of image processing via deep learning. By combining the inverse residual architecture with coordinate attention (CA) mechanism, a coordinate attention mobile (CAM) backbone network is constructed for feature extraction. Then, in order to solve the small object detection problem, the multi-scale strategy is developed by introducing the CA into the cross-layer information flow to improve the quality of feature extraction and augment the representation ability on multi-scale features. Hereafter, the multi-scale feature is integrated to design a novel bidirectional weighted feature pyramid network (BWFPN) to improve the model detection accuracy without increasing much computational burden. From the comparative experimental results on open source datasets, the effectiveness of the developed lightweight CNN is evaluated, and the detection accuracy attains on par with the state-of-the-art (SOTA) model with less parameters and calculation.},
+	language = {en},
+	number = {9},
+	urldate = {2025-03-23},
+	journal = {Artificial Intelligence Review},
+	author = {Zhang, Dehua and Hao, Xinyuan and Wang, Dechen and Qin, Chunbin and Zhao, Bo and Liang, Linlin and Liu, Wei},
+	month = sep,
+	year = {2023},
+	keywords = {Artificial Intelligence, Attention mechanism, Feature pyramid networks, Lightweight convolutional neural networks, Surface defect detection},
+	pages = {10651--10677},
+	file = {Full Text PDF:C\:\\Users\\SFI19\\Zotero\\storage\\EJQ8PAKB\\Zhang et al. - 2023 - An efficient lightweight convolutional neural network for industrial surface defect detection.pdf:application/pdf},
+}
+
+@article{litjens_survey_2017,
+	title = {A {Survey} on {Deep} {Learning} in {Medical} {Image} {Analysis}},
+	volume = {42},
+	issn = {13618415},
+	url = {http://arxiv.org/abs/1702.05747},
+	doi = {10.1016/j.media.2017.07.005},
+	abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.},
+	urldate = {2025-03-23},
+	journal = {Medical Image Analysis},
+	author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and Laak, Jeroen A. W. M. van der and Ginneken, Bram van and Sánchez, Clara I.},
+	month = dec,
+	year = {2017},
+	note = {arXiv:1702.05747 [cs]},
+	keywords = {Computer Science - Computer Vision and Pattern Recognition},
+	pages = {60--88},
+	annote = {Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 2017},
+	file = {Preprint PDF:C\:\\Users\\SFI19\\Zotero\\storage\\DT6DHLHY\\Litjens et al. - 2017 - A Survey on Deep Learning in Medical Image Analysis.pdf:application/pdf;Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\DSJ5RKP6\\1702.html:text/html},
+}
+
+@article{maimaitijiang_soybean_2020,
+	title = {Soybean yield prediction from {UAV} using multimodal data fusion and deep learning},
+	url = {https://www.academia.edu/84238554/Soybean_yield_prediction_from_UAV_using_multimodal_data_fusion_and_deep_learning},
+	abstract = {Preharvest crop yield prediction is critical for grain policy making and food security. Early estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping and precision agriculture. New developments in Unmanned},
+	urldate = {2025-03-23},
+	journal = {Remote Sensing of Environment},
+	author = {Maimaitijiang, Maitiniyazi},
+	month = jan,
+	year = {2020},
+	file = {PDF:C\:\\Users\\SFI19\\Zotero\\storage\\PB6J69JW\\Maimaitijiang - 2020 - Soybean yield prediction from UAV using multimodal data fusion and deep learning.pdf:application/pdf;Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\SYJLAK92\\Soybean_yield_prediction_from_UAV_using_multimodal_data_fusion_and_deep_learning.html:text/html},
+}
+
+@misc{janai_computer_2021,
+	title = {Computer {Vision} for {Autonomous} {Vehicles}: {Problems}, {Datasets} and {State} of the {Art}},
+	shorttitle = {Computer {Vision} for {Autonomous} {Vehicles}},
+	url = {http://arxiv.org/abs/1704.05519},
+	doi = {10.48550/arXiv.1704.05519},
+	abstract = {Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This book attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.},
+	urldate = {2025-03-23},
+	publisher = {arXiv},
+	author = {Janai, Joel and Güney, Fatma and Behl, Aseem and Geiger, Andreas},
+	month = mar,
+	year = {2021},
+	note = {arXiv:1704.05519 [cs]},
+	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics},
+	file = {Preprint PDF:C\:\\Users\\SFI19\\Zotero\\storage\\J5BRT4MJ\\Janai et al. - 2021 - Computer Vision for Autonomous Vehicles Problems, Datasets and State of the Art.pdf:application/pdf;Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\PTNC6R8L\\1704.html:text/html},
+}
+
+@misc{ren_faster_2016,
+	title = {Faster {R}-{CNN}: {Towards} {Real}-{Time} {Object} {Detection} with {Region} {Proposal} {Networks}},
+	shorttitle = {Faster {R}-{CNN}},
+	url = {http://arxiv.org/abs/1506.01497},
+	doi = {10.48550/arXiv.1506.01497},
+	abstract = {State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.},
+	urldate = {2025-03-23},
+	publisher = {arXiv},
+	author = {Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
+	month = jan,
+	year = {2016},
+	note = {arXiv:1506.01497 [cs]},
+	keywords = {Computer Science - Computer Vision and Pattern Recognition},
+	annote = {Comment: Extended tech report},
+	file = {Preprint PDF:C\:\\Users\\SFI19\\Zotero\\storage\\PKY5AU96\\Ren et al. - 2016 - Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks.pdf:application/pdf;Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\X8PBIK44\\1506.html:text/html},
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+@misc{abadi_tensorflow_2016,
+	title = {{TensorFlow}: {A} system for large-scale machine learning},
+	shorttitle = {{TensorFlow}},
+	url = {http://arxiv.org/abs/1605.08695},
+	doi = {10.48550/arXiv.1605.08695},
+	abstract = {TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.},
+	urldate = {2025-03-23},
+	publisher = {arXiv},
+	author = {Abadi, Martín and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and Kudlur, Manjunath and Levenberg, Josh and Monga, Rajat and Moore, Sherry and Murray, Derek G. and Steiner, Benoit and Tucker, Paul and Vasudevan, Vijay and Warden, Pete and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang},
+	month = may,
+	year = {2016},
+	note = {arXiv:1605.08695 [cs]},
+	keywords = {Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing},
+	annote = {Comment: 18 pages, 9 figures; v2 has a spelling correction in the metadata},
+	file = {Preprint PDF:C\:\\Users\\SFI19\\Zotero\\storage\\ND7JHGWD\\Abadi et al. - 2016 - TensorFlow A system for large-scale machine learning.pdf:application/pdf;Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\7IRZAXVR\\1605.html:text/html},
+}
+
+
+@book{ragan-kelley_halide_2013,
+	title = {Halide: {A} {Language} and {Compiler} for {Optimizing} {Parallelism}, {Locality}, and {Recomputation} in {Image} {Processing} {Pipelines}},
+	volume = {48},
+	shorttitle = {Halide},
+	abstract = {Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values.
+We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.},
+	author = {Ragan-Kelley, Jonathan and Barnes, Connelly and Adams, Andrew and Paris, Sylvain and Durand, Frédo and Amarasinghe, Saman},
+	month = jun,
+	year = {2013},
+	doi = {10.1145/2499370.2462176},
+	note = {Journal Abbreviation: ACM SIGPLAN Notices
+Pages: 530
+Publication Title: ACM SIGPLAN Notices},
+	file = {Full Text:C\:\\Users\\SFI19\\Zotero\\storage\\62D2CBIL\\Ragan-Kelley et al. - 2013 - Halide A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Pro.pdf:application/pdf},
+}
+
+@book{russell_artificial_2016,
+	address = {Boston},
+	edition = {Third edition, Global edition},
+	title = {Artificial intelligence a modern approach},
+	isbn = {978-1-292-15396-4},
+	url = {http://www.gbv.de/dms/tib-ub-hannover/848811429.pdf},
+	abstract = {Hier auch später erschienene, unveränderte Nachdrucke},
+	urldate = {2025-03-23},
+	publisher = {Pearson},
+	author = {Russell, Stuart J. and Norvig, Peter and Davis, Ernest and Edwards, Douglas},
+	year = {2016},
+	keywords = {Artificial intelligence, Künstliche Intelligenz, Precht, Richard David},
+	file = {Artificial Intelligence-A Modern Approach (3rd Edition) ( PDFDrive ).pdf:C\:\\Users\\SFI19\\Zotero\\storage\\MX8PZ6JQ\\Artificial Intelligence-A Modern Approach (3rd Edition) ( PDFDrive ).pdf:application/pdf},
+}
+
-- 
GitLab