diff --git a/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl b/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl
index 65456746d19ed3be1137a159132ea399f31520f0..8d4497527240068421cd3f2d44520e9159e0f110 100644
--- a/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl
+++ b/Fazeli_Shahroudi-Sepehr-Mastersthesis.bbl
@@ -1,5 +1,5 @@
% Generated by IEEEtran.bst, version: 1.14 (2015/08/26)
-\begin{thebibliography}{1}
+\begin{thebibliography}{10}
\providecommand{\url}[1]{#1}
\csname url@samestyle\endcsname
\providecommand{\newblock}{\relax}
@@ -21,6 +21,31 @@
\providecommand{\BIBdecl}{\relax}
\BIBdecl
+\bibitem{kulpa_universal_1981}
+Z.~Kulpa, ``\BIBforeignlanguage{en}{Universal digital image processing systems
+ in europe — {A} comparative survey},'' in
+ \emph{\BIBforeignlanguage{en}{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{ma_new_2024}
+\BIBentryALTinterwordspacing
+X.~Ma, Y.~Jiang, H.~Liu, C.~Zhou, and K.~Gu, ``A {New} {Image} {Quality}
+ {Database} for {Multiple} {Industrial} {Processes},'' Feb. 2024,
+ arXiv:2401.13956 [cs]. [Online]. Available:
+ \url{http://arxiv.org/abs/2401.13956}
+\BIBentrySTDinterwordspacing
+
+\bibitem{chisholm_fpga-based_2020}
+\BIBentryALTinterwordspacing
+T.~Chisholm, R.~Lins, and S.~Givigi, ``{FPGA}-{Based} {Design} for
+ {Real}-{Time} {Crack} {Detection} {Based} on {Particle} {Filter},''
+ \emph{IEEE Transactions on Industrial Informatics}, vol.~16, no.~9, pp.
+ 5703--5711, Sep. 2020, conference Name: IEEE Transactions on Industrial
+ Informatics. [Online]. Available:
+ \url{https://ieeexplore.ieee.org/document/8888239}
+\BIBentrySTDinterwordspacing
+
\bibitem{ferreira_generic_2024}
D.~Ferreira, F.~Moutinho, J.~P. Matos-Carvalho, M.~Guedes, and P.~Deusdado,
``\BIBforeignlanguage{eng}{Generic {FPGA} {Pre}-{Processing} {Image}
@@ -28,4 +53,100 @@ D.~Ferreira, F.~Moutinho, J.~P. Matos-Carvalho, M.~Guedes, and P.~Deusdado,
\emph{\BIBforeignlanguage{eng}{Sensors (Basel, Switzerland)}}, vol.~24,
no.~18, p. 6101, Sep. 2024.
+\bibitem{perez_super-resolution_2014}
+\BIBentryALTinterwordspacing
+J.~Pérez, E.~Magdaleno, F.~Pérez, M.~RodrÃguez, D.~Hernández, and
+ J.~Corrales, ``\BIBforeignlanguage{en}{Super-{Resolution} in {Plenoptic}
+ {Cameras} {Using} {FPGAs}},'' \emph{\BIBforeignlanguage{en}{Sensors}},
+ vol.~14, no.~5, pp. 8669--8685, May 2014, number: 5 Publisher:
+ Multidisciplinary Digital Publishing Institute. [Online]. Available:
+ \url{https://www.mdpi.com/1424-8220/14/5/8669}
+\BIBentrySTDinterwordspacing
+
+\bibitem{rao_comparative_2023}
+\BIBentryALTinterwordspacing
+M.~N. Rao, ``\BIBforeignlanguage{en}{A {Comparative} {Analysis} of {Deep}
+ {Learning} {Frameworks} and {Libraries}},''
+ \emph{\BIBforeignlanguage{en}{International Journal of Intelligent Systems
+ and Applications in Engineering}}, vol.~11, no.~2s, pp. 337--342, Jan. 2023,
+ number: 2s. [Online]. Available:
+ \url{https://ijisae.org/index.php/IJISAE/article/view/2707}
+\BIBentrySTDinterwordspacing
+
+\bibitem{ciora_industrial_2014}
+\BIBentryALTinterwordspacing
+R.~A. Ciora and C.~M. Simion, ``\BIBforeignlanguage{en}{Industrial
+ {Applications} of {Image} {Processing}},'' \emph{\BIBforeignlanguage{en}{ACTA
+ Universitatis Cibiniensis}}, vol.~64, no.~1, pp. 17--21, Nov. 2014. [Online].
+ Available: \url{https://www.sciendo.com/article/10.2478/aucts-2014-0004}
+\BIBentrySTDinterwordspacing
+
+\bibitem{sandvik_comparative_2024}
+\BIBentryALTinterwordspacing
+Y.~J. Sandvik, C.~M. Futsæther, K.~H. Liland, and O.~Tomic,
+ ``\BIBforeignlanguage{en}{A {Comparative} {Literature} {Review} of {Machine}
+ {Learning} and {Image} {Processing} {Techniques} {Used} for {Scaling} and
+ {Grading} of {Wood} {Logs}},'' \emph{\BIBforeignlanguage{en}{Forests}},
+ vol.~15, no.~7, p. 1243, Jul. 2024, number: 7 Publisher: Multidisciplinary
+ Digital Publishing Institute. [Online]. Available:
+ \url{https://www.mdpi.com/1999-4907/15/7/1243}
+\BIBentrySTDinterwordspacing
+
+\bibitem{sardar_role_2012}
+\BIBentryALTinterwordspacing
+H.~Sardar, ``A role of computer system for comparative analysis using image
+ processing to promote agriculture business,'' \emph{International journal of
+ engineering research and technology}, Nov. 2012. [Online]. Available:
+ \url{https://www.semanticscholar.org/paper/A-role-of-computer-system-for-comparative-analysis-Sardar/6e2fd48a1025b68951f511abe05f8451f753eb47}
+\BIBentrySTDinterwordspacing
+
+\bibitem{vieira_performance_2024}
+\BIBentryALTinterwordspacing
+R.~Vieira, D.~Silva, E.~Ribeiro, L.~Perdigoto, and P.~J. Coelho,
+ ``\BIBforeignlanguage{en}{Performance {Evaluation} of {Computer} {Vision}
+ {Algorithms} in a {Programmable} {Logic} {Controller}: {An} {Industrial}
+ {Case} {Study}},'' \emph{\BIBforeignlanguage{en}{Sensors}}, vol.~24, no.~3,
+ p. 843, Jan. 2024, number: 3 Publisher: Multidisciplinary Digital Publishing
+ Institute. [Online]. Available: \url{https://www.mdpi.com/1424-8220/24/3/843}
+\BIBentrySTDinterwordspacing
+
+\bibitem{wu_precision_2022}
+\BIBentryALTinterwordspacing
+S.~Wu, H.~Yang, X.~Liu, and R.~Jia, ``\BIBforeignlanguage{English}{Precision
+ control of polyurethane filament drafting and winding based on machine
+ vision},'' \emph{\BIBforeignlanguage{English}{Frontiers in Bioengineering and
+ Biotechnology}}, vol.~10, Sep. 2022, publisher: Frontiers. [Online].
+ Available:
+ \url{https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.978212/full}
+\BIBentrySTDinterwordspacing
+
+\bibitem{zhu_machine_2022}
+\BIBentryALTinterwordspacing
+Q.~Zhu, Y.~Zhang, J.~Luan, and L.~Hu, ``\BIBforeignlanguage{en}{A {Machine}
+ {Vision} {Development} {Framework} for {Product} {Appearance} {Quality}
+ {Inspection}},'' \emph{\BIBforeignlanguage{en}{Applied Sciences}}, vol.~12,
+ no.~22, p. 11565, Jan. 2022, number: 22 Publisher: Multidisciplinary Digital
+ Publishing Institute. [Online]. Available:
+ \url{https://www.mdpi.com/2076-3417/12/22/11565}
+\BIBentrySTDinterwordspacing
+
+\bibitem{reis_developments_2023}
+\BIBentryALTinterwordspacing
+M.~J. C.~S. Reis, ``\BIBforeignlanguage{en}{Developments of {Computer} {Vision}
+ and {Image} {Processing}: {Methodologies} and {Applications}},''
+ \emph{\BIBforeignlanguage{en}{Future Internet}}, vol.~15, no.~7, p. 233, Jul.
+ 2023, number: 7 Publisher: Multidisciplinary Digital Publishing Institute.
+ [Online]. Available: \url{https://www.mdpi.com/1999-5903/15/7/233}
+\BIBentrySTDinterwordspacing
+
+\bibitem{ziaja_benchmarking_2021}
+\BIBentryALTinterwordspacing
+M.~Ziaja, P.~Bosowski, M.~Myller, G.~Gajoch, M.~Gumiela, J.~Protich, K.~Borda,
+ D.~Jayaraman, R.~Dividino, and J.~Nalepa,
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+ {Space} {Applications}},'' \emph{\BIBforeignlanguage{en}{Remote Sensing}},
+ vol.~13, no.~19, p. 3981, Oct. 2021. [Online]. Available:
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+\BIBentrySTDinterwordspacing
+
\end{thebibliography}
diff --git a/sections/Chapter-1-sections/Related-Work.tex b/sections/Chapter-1-sections/Related-Work.tex
index 12a56fa4a42802f5c3a1808a7d89bf61cc8a3b01..a6207b949cecdf47f73053d771b1921637146d09 100644
--- a/sections/Chapter-1-sections/Related-Work.tex
+++ b/sections/Chapter-1-sections/Related-Work.tex
@@ -1,35 +1,25 @@
\section{Related Work}
-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) seminal review of digital image processing systems in Europe, laid the groundwork for understanding the challenges of standardization and performance evaluation in these systems.
+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.
-\subsection{Broad Perspectives on Industrial Image Processing}
+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) 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) 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.
-Chisholm et al. (2020) and Ferreira et al. (2024) extend these discussions by focusing on the implementation of real-time image processing systems using FPGAs. Chisholm et al. (2020) illustrate a real-time crack detection system employing particle filters, highlighting the challenges of meeting stringent timing constraints in industrial settings. Ferreira et al. (2024), 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.
-\subsection{Comparative Studies and Historical Reviews}
+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.
-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) remains an important historical reference, offering insights into the evolution of image processing systems and highlighting persistent issues such as limited standardization and documentation.
+Several studies have explored niche industrial applications where image processing plays a critical role. Ciora and Simion (2014) \cite{ciora_industrial_2014} provide a broad overview of the applications of image processing in industrial engineering, covering areas from automated visual inspection to process control. Their comprehensive review underscores the necessity of robust, efficient image processing systems that integrate seamlessly with industrial control mechanisms.
-Pérez et al. (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) 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.
+In a more focused domain, Sandvik et al. (2024) \cite{sandvik_comparative_2024} review machine learning and image processing techniques for wood log scaling and grading. Their systematic categorization of methodologies offers a template for benchmarking approaches that combine computer vision with domain-specific performance metrics. Sardar (2012) \cite{sardar_role_2012} examines the use of image processing for quality analysis in agriculture, further highlighting the versatility of these technologies across different industrial sectors.
-\subsection{Niche Applications and Domain-Specific Evaluations}
+Vieira et al. (2024) \cite{vieira_performance_2024} address the challenges of deploying image processing algorithms on Programmable Logic Controllers (PLCs), which are prevalent in industrial control systems. Their work illustrates the trade-offs between processing speed, implementation complexity, and system robustness when operating in resource-constrained environments. Wu et al. (2022) \cite{wu_precision_2022} and Zhu et al. (2022) \cite{zhu_machine_2022} then delve into specific industrial applications—precision control in filament drafting and product appearance quality inspection, respectively—demonstrating the critical impact of real-time processing and integration on system performance.
-Several studies have explored niche industrial applications where image processing plays a critical role. Ciora and Simion (2014) provide a broad overview of the applications of image processing in industrial engineering, covering areas from automated visual inspection to process control. Their comprehensive review underscores the necessity of robust, efficient image processing systems that integrate seamlessly with industrial control mechanisms.
-
-In a more focused domain, Sandvik et al. (2024) review machine learning and image processing techniques for wood log scaling and grading. Their systematic categorization of methodologies offers a template for benchmarking approaches that combine computer vision with domain-specific performance metrics. Sardar (2012) examines the use of image processing for quality analysis in agriculture, further highlighting the versatility of these technologies across different industrial sectors.
-
-Vieira et al. (2024) address the challenges of deploying image processing algorithms on Programmable Logic Controllers (PLCs), which are prevalent in industrial control systems. Their work illustrates the trade-offs between processing speed, implementation complexity, and system robustness when operating in resource-constrained environments. Wu et al. (2022) and Zhu et al. (2022) then delve into specific industrial applications—precision control in filament drafting and product appearance quality inspection, respectively—demonstrating the critical impact of real-time processing and integration on system performance.
-
-\subsection{Benchmarking and Performance Evaluation in Contemporary Research}
-
-At the forefront of current research are studies that provide robust benchmarking frameworks. Reis (2023) offers an overview of recent developments in computer vision and image processing methodologies, pointing out the increasing integration of artificial intelligence with classical approaches. This evolution is complemented by Ziaja et al. (2021), whose work on benchmarking deep learning for on-board space applications provides a rigorous framework for evaluating execution time, resource utilization, and overall performance under constrained hardware conditions.
+At the forefront of current research are studies that provide robust benchmarking frameworks. Reis (2023) \cite{reis_developments_2023} offers an overview of recent developments in computer vision and image processing methodologies, pointing out the increasing integration of artificial intelligence with classical approaches. This evolution is complemented by Ziaja et al. (2021) \cite{ziaja_benchmarking_2021}, whose work on benchmarking deep learning for on-board space applications provides a rigorous framework for evaluating execution time, resource utilization, and overall performance under constrained hardware conditions.
These contemporary evaluations are essential for highlighting the limitations of existing approaches. While many studies focus on performance metrics such as processing speed and memory efficiency, few have systematically integrated these factors with ease of integration and system robustness in industrial settings. This gap in the literature motivates the present study, which aims to establish a comprehensive benchmarking approach that encompasses both hardware acceleration and software flexibility.
-\subsection{Synthesis and Research Gap}
-
-In summary, the reviewed literature presents a rich tapestry of methodologies and evaluations that span a broad spectrum of industrial image processing applications. Early foundational works provided historical context and identified critical challenges, while subsequent studies advanced the field by integrating hardware acceleration, deep learning, and niche industrial applications into comprehensive performance evaluations. Despite these advances, a clear gap remains in the standardization of benchmarking protocols that simultaneously address performance, resource efficiency, and integration challenges in real-world industrial settings. This thesis proposes a novel benchmarking approach that differentiates itself by not only comparing the computational performance of various image processing libraries but also by evaluating their ease of integration into complex industrial workflows. By doing so, the study seeks to provide actionable insights for practitioners and pave the way for the next generation of robust, efficient, and versatile image processing solutions.
+In summary, the reviewed literature presents a rich tapestry of methodologies and evaluations that span a broad spectrum of industrial image processing applications. Early foundational works provided historical context and identified critical challenges, while subsequent studies advanced the field by integrating hardware acceleration, deep learning, and niche industrial applications into comprehensive performance evaluations. Despite these advances, a clear gap remains in the standardization of benchmarking protocols that address performance, resource efficiency, and integration challenges in real-world industrial settings. This thesis proposes a novel benchmarking approach that differentiates itself by not only comparing the computational performance of various image processing libraries but also by evaluating their ease of integration into complex industrial workflows. By doing so, the study seeks to provide actionable insights for practitioners and pave the way for the next generation of robust, efficient, and versatile image processing solutions.