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.~Rodríguez, 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,
+  ``\BIBforeignlanguage{en}{Benchmarking {Deep} {Learning} for {On}-{Board}
+  {Space} {Applications}},'' \emph{\BIBforeignlanguage{en}{Remote Sensing}},
+  vol.~13, no.~19, p. 3981, Oct. 2021. [Online]. Available:
+  \url{https://www.mdpi.com/2072-4292/13/19/3981}
+\BIBentrySTDinterwordspacing
+
 \end{thebibliography}
diff --git a/sections/Chapter-1-sections/Related-Work.tex b/sections/Chapter-1-sections/Related-Work.tex
index 7d2393700efa87c91fefc9a1b0e8014c7d1d2f9e..a6207b949cecdf47f73053d771b1921637146d09 100644
--- a/sections/Chapter-1-sections/Related-Work.tex
+++ b/sections/Chapter-1-sections/Related-Work.tex
@@ -1,223 +1,25 @@
 \section{Related Work}
 
-In this chapter, we review and synthesize research studies that relate to the evaluation of image processing libraries and their applications in industrial and specialized contexts. The selected literature spans diverse topics—from hardware acceleration and real-time processing to quality assessment databases and comprehensive machine vision frameworks. Although not every study addresses the thesis topic directly, each work contributes insights into performance, resource efficiency, and integration challenges. These aspects are critical when comparing image processing libraries for industrial applications.
+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.
 
-\subsection{Distributed Large-Scale Graph Processing on FPGAs (Sahebi et al., 2023)}
+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.
 
-Sahebi et al. (2023) present an innovative approach to large-scale graph processing using FPGAs and distributed computing frameworks. Although the paper focuses on graph data rather than traditional image processing, the methodologies and optimization strategies discussed are highly pertinent to industrial image processing tasks. The authors introduce a novel model that leverages Hadoop to distribute graph processing workloads across multiple workers, including FPGAs, which significantly improves processing speed and efficiency.
+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.
 
-The paper details how the proposed system partitions large graphs into smaller chunks—an approach that minimizes external memory accesses, which is critical when dealing with limited on-chip memory. This technique parallels the challenges encountered in processing high-resolution industrial images, where efficient data partitioning is vital to reduce latency. The study demonstrates speedups of up to 2x, 4.4x, and 26x compared to traditional CPU, GPU, and FPGA solutions, respectively. These improvements underscore the potential benefits of hardware acceleration, a concept that is directly transferable to the evaluation of image processing libraries.
+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.
 
-Moreover, the work emphasizes resource efficiency and the importance of minimizing memory overhead. The FPGA-based solution required careful design to ensure that processing kernels used minimal resources, thereby enabling increased parallelism. For industrial applications where large image datasets must be processed in real time, similar design principles—such as minimizing data transfers and efficiently partitioning workloads—are crucial. By adapting these principles, the current thesis evaluates how various image processing libraries can leverage hardware acceleration to achieve improved performance under resource constraints.
+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.
 
-In summary, Sahebi et al. provide valuable insights into distributed processing and hardware optimization techniques. Their research serves as a foundational reference for understanding how similar strategies can be employed to enhance the performance and resource efficiency of image processing libraries in industrial contexts.
+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.
 
-%%%
+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.
 
-\subsection{A New Image Quality Database for Multiple Industrial Processes (Ma et al., 2024)}
+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.
 
-Ma et al. (2024) introduce the Industrial Process Image Database (IPID), a specialized resource designed to assess image quality in complex industrial environments. The authors generated a database of 3000 distorted images derived from 50 high-quality source images, incorporating a range of distortions in terms of type and degree. This database aims to provide a standardized benchmark for evaluating image quality assessment (IQA) algorithms, which is crucial for applications where visual inspection plays a key role.
+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.
 
-The study’s methodology involves subjective scoring experiments that align objective quality metrics with human perception. Such alignment is particularly important in industrial settings where visual quality is paramount. The IPID includes images captured under diverse lighting conditions, atmospheric variations, and realistic industrial scenarios (e.g., production lines and warehouses). This diversity ensures that the benchmark reflects the multifaceted nature of real-world industrial imaging challenges.
+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.
 
-The work reveals that many existing IQA algorithms exhibit low correlation with subjective assessments, indicating that current methods struggle to capture the nuances of image quality as perceived by human operators. For the present thesis, these findings underscore the importance of not only evaluating raw performance metrics of image processing libraries (such as speed and memory usage) but also considering the impact on image quality, especially in applications where image distortion can affect critical outcomes.
 
-Ma et al.’s contribution is significant because it establishes a robust framework for benchmarking image processing techniques against a realistic and diverse dataset. The IPID serves as a critical tool for comparing how different libraries manage image distortions and maintain quality under industrial conditions. Such a framework is directly applicable to the current research, which seeks to evaluate the robustness and efficiency of various image processing libraries in handling complex, real-world data.
-
-%%%
-
-\subsection{FPGA-Based Design for Real-Time Crack Detection Using Particle Filters (Chisholm et al., 2020)}
-
-Chisholm et al. (2020) focus on the development of a real-time crack detection system using FPGAs, which is an exemplary case of applying image processing for industrial quality control. The authors implement particle filter-based algorithms to identify and measure cracks in real time, a task critical for maintenance and safety in industrial infrastructures.
-
-The study is notable for its comprehensive evaluation of both detection accuracy and computational performance. By comparing parameters such as measurement precision, processing speed, physical footprint, and energy consumption, the authors demonstrate the advantages of employing hardware-accelerated solutions in time-sensitive applications. Their system achieves real-time processing by tightly integrating the image processing algorithms with FPGA hardware, ensuring minimal latency.
-
-This work is directly relevant to the current thesis because it highlights how real-time image processing can be achieved in resource-constrained industrial environments. The study discusses the challenges associated with real-world implementation, including the need to process large image datasets under stringent time constraints. The authors emphasize the importance of optimizing algorithms for parallel execution, which directly informs the evaluation of image processing libraries in terms of their ability to support hardware acceleration and real-time processing.
-
-Moreover, the paper outlines the integration of the detection system with broader industrial control mechanisms, illustrating the need for seamless interoperability between image processing libraries and other system components. Such integration is a key factor in the present research, as the overall effectiveness of an image processing library in an industrial setting depends not only on its computational performance but also on its ease of integration into existing industrial workflows.
-
-In conclusion, Chisholm et al. provide a compelling demonstration of hardware-accelerated, real-time image processing in an industrial application. Their findings contribute important criteria—such as processing speed, accuracy, and energy efficiency—that are used to benchmark and evaluate the image processing libraries discussed in this thesis.
-
-%%%
-
-\subsection{Industrial Applications of Image Processing (Ciora and Simion, 2014)}
-
-Ciora and Simion (2014) offer a broad overview of the applications of image processing in industrial engineering. Their review examines a wide range of practical implementations, including automated visual inspection, process control, part identification, and robotic guidance. The paper serves as a foundational reference by contextualizing the role of image processing in modern industrial settings.
-
-The authors highlight that industrial image processing systems must meet rigorous standards of accuracy and reliability. They discuss various techniques—such as feature extraction, object recognition, and pattern recognition—and illustrate how these methods are applied in real-world industrial scenarios. For instance, the paper reviews the use of machine vision for monitoring assembly lines, detecting defects in manufactured parts, and guiding robotic systems. These applications underscore the critical role that image processing plays in ensuring quality control and operational efficiency.
-
-One of the key contributions of this work is its emphasis on the integration of image processing algorithms with industrial control systems. The authors note that a successful image processing solution in an industrial environment must not only perform well in isolation but also interface effectively with hardware and software systems that drive production processes. This insight is directly relevant to the present thesis, which evaluates image processing libraries not just on performance metrics but also on their compatibility with industrial applications.
-
-Additionally, Ciora and Simion discuss the challenges inherent in implementing image processing systems, such as the need for robust data acquisition and handling large volumes of image data in real time. These challenges highlight the importance of developing efficient algorithms and utilizing hardware acceleration—key themes that are explored in the current research.
-
-Overall, this comprehensive review provides essential background information on the state of industrial image processing. It establishes the importance of robust, efficient, and well-integrated image processing systems, thereby setting the stage for the subsequent evaluation of various image processing libraries within this thesis.
-
-%%%
-
-\subsection{Generic FPGA Pre-Processing Image Library for Industrial Vision Systems (Ferreira et al., 2024)}
-
-Ferreira et al. (2024) focus on the development of a generic library of pre-processing filters designed specifically for implementation on FPGAs within industrial vision systems. The paper addresses the critical need for accelerating image processing tasks to meet the demands of modern industrial applications. By leveraging the parallel processing capabilities of FPGAs, the authors demonstrate substantial improvements in processing times, reducing latency from milliseconds to nanoseconds in certain cases.
-
-A key aspect of the study is its emphasis on resource efficiency. The authors detail how their FPGA-based solution minimizes memory accesses and optimizes data partitioning to reduce external memory overhead. These strategies are particularly relevant to industrial scenarios, where high-resolution images and large datasets are common, and any delay in processing can result in significant bottlenecks.
-
-The experimental results presented in the paper reveal that the proposed pre-processing library significantly outperforms traditional CPU and GPU implementations under specific conditions. The study also discusses the trade-offs involved in developing FPGA solutions, notably the longer development time and the requirement for specialized hardware description languages. However, the performance gains achieved through hardware acceleration justify these additional efforts, especially in time-critical industrial applications.
-
-This work is directly applicable to the thesis, as it highlights the importance of optimizing image processing pipelines through hardware acceleration. The detailed discussion of data partitioning strategies, memory management, and resource allocation provides a framework that can be used to evaluate the resource efficiency of various image processing libraries. Furthermore, the emphasis on reducing processing time and achieving high throughput aligns with the thesis’s objectives of comparing library performance in real-world industrial scenarios.
-
-In summary, Ferreira et al. make a significant contribution by demonstrating how FPGA-based pre-processing can be leveraged to enhance the performance of image processing systems. Their insights into hardware acceleration, memory optimization, and efficient data partitioning are critical for understanding the challenges and opportunities associated with modern industrial image processing.
-
-%%%
-
-\subsection{Universal Digital Image Processing Systems in Europe – A Comparative Survey (Kulpa, 1981)}
-
-Although dated, Kulpa’s (1981) survey remains a seminal work in the field of digital image processing. This early comparative study provides a historical perspective on the evolution of image processing systems in Europe and serves as an important reference for understanding the foundational challenges that continue to influence modern systems.
-
-Kulpa’s survey evaluates eleven universal image processing systems developed across various European countries. The study categorizes these systems based on their design goals, technological approaches, and application domains. A significant observation made by Kulpa is that many of these early systems were designed in an ad hoc manner, with limited documentation and a lack of standardized evaluation methodologies. This lack of standardization led to difficulties in comparing system performance and functionality, a challenge that persists in the evaluation of contemporary image processing libraries.
-
-The survey also highlights the diversity of image processing approaches, ranging from systems developed for research purposes to those intended for commercial applications. Kulpa emphasizes the importance of systematic software design and clear documentation—principles that remain crucial in modern software engineering. The insights provided in this survey lay the groundwork for the evolution of more structured and comparable image processing systems.
-
-For the current thesis, Kulpa’s work offers a valuable historical context that underscores the progress made over the past decades. It also reinforces the need for standardized benchmarking and systematic evaluation of image processing libraries, which is a central theme in the current research. By understanding the challenges encountered by early systems, researchers can better appreciate the trade-offs and design decisions inherent in modern image processing frameworks.
-
-In essence, this historical survey not only contextualizes the evolution of image processing systems but also highlights enduring challenges—such as standardization and systematic evaluation—that are critical to the development and assessment of contemporary image processing libraries.
-
-%%%
-
-\subsection{Image Processing Libraries: A Comparative Review (Lai et al., 2001)}
-
-Lai et al. (2001) provide an in-depth comparative review of several image processing library implementations, including Datacube’s ImageFlow, the Vector, Signal and Image Processing Library (VSIPL), and Vision with Generic Algorithms (VIGRA). This review is particularly valuable as it examines different design philosophies and approaches to building image processing libraries, ranging from vendor-specific solutions to hardware-neutral and generic programming-based libraries.
-
-The paper discusses the strengths and weaknesses of each implementation. For instance, Datacube’s ImageFlow is designed to leverage specific hardware capabilities, offering optimized performance through vendor-specific enhancements. In contrast, VSIPL emphasizes portability and hardware neutrality, ensuring that the library can be deployed across various platforms without significant modifications. VIGRA, built on generic programming principles, aims to offer flexibility and ease of integration without incurring substantial performance penalties.
-
-The comparative analysis in this study focuses on several key criteria, including processing speed, memory management, ease of integration, and the flexibility of the programming model. Lai et al. argue that the choice between a hardware-specific solution and a generic, portable one depends on the specific application requirements. For industrial applications, where performance and resource efficiency are critical, the trade-offs between these approaches must be carefully evaluated.
-
-This paper contributes significantly to the literature by providing a framework for understanding how different design choices impact overall performance and usability. The insights regarding vendor-specific optimizations versus generic programming approaches directly inform the evaluation criteria for the current thesis. By comparing these distinct paradigms, the study underscores the importance of balancing performance with portability and ease of integration—a balance that is central to the comparative evaluation of image processing libraries in this research.
-
-Overall, Lai et al. offer a comprehensive review that highlights the evolution and diversity of image processing libraries. Their analysis provides a solid foundation for understanding the trade-offs involved in library design, which is instrumental for evaluating and selecting the most appropriate image processing solution for industrial applications.
-
-%%%
-
-\subsection{Super-Resolution in Plenoptic Cameras Using FPGAs (Pérez et al., 2014)}
-
-Pérez et al. (2014) explore the implementation of super-resolution algorithms for plenoptic cameras using FPGA-based solutions. Although the application domain—plenoptic imaging—differs from general industrial image processing, the study’s focus on leveraging hardware acceleration to improve image quality and processing speed is directly relevant to the present thesis.
-
-The authors demonstrate how FPGAs can be used to implement super-resolution algorithms, which enhance the spatial resolution of images captured by plenoptic cameras. Their work highlights several advantages of FPGA-based solutions, including parallel processing capabilities, low power consumption, and the ability to perform complex image enhancement tasks in real time. The study also provides a detailed account of the trade-offs involved in implementing such algorithms, including the challenges of balancing processing speed with hardware resource constraints.
-
-One of the key contributions of this paper is its demonstration of how hardware acceleration can significantly reduce processing times while maintaining high image quality. The authors report that their FPGA implementation achieved substantial performance improvements compared to traditional CPU-based methods, a finding that underscores the potential benefits of integrating hardware acceleration into image processing pipelines.
-
-For the current thesis, Pérez et al.’s research offers important insights into the design and optimization of image processing systems for high-performance applications. Their emphasis on parallel processing and efficient resource management provides a valuable framework for evaluating how different image processing libraries can leverage hardware acceleration features. Furthermore, the study’s detailed performance analysis, which considers both execution time and resource utilization, aligns closely with the evaluation criteria used in this thesis.
-
-In conclusion, the work by Pérez et al. serves as a compelling example of how FPGA-based hardware acceleration can enhance the capabilities of image processing algorithms. The lessons learned from this study—particularly regarding the optimization of processing pipelines and the efficient use of hardware resources—are directly applicable to the comparative evaluation of image processing libraries in industrial settings.
-
-Below is Part 2 of the expanded Related Work chapter, covering Sections 2.9 through 2.16 and concluding with an overall synthesis.
-
-%%%
-
-\subsection{Comparative Analysis of Deep Learning Frameworks and Libraries (Rao, 2023)}
-
-Rao (2023) provides a comprehensive comparison of deep learning frameworks—including TensorFlow, PyTorch, Keras, MXNet, and Caffe—focusing on criteria such as performance, ease of use, documentation, and community support. Although the primary focus is on deep learning rather than traditional image processing, the methodology employed in this study offers valuable insights for evaluating software libraries.
-
-The paper benchmarks each framework using standardized tasks and datasets, assessing execution speed and memory consumption. Rao’s analysis reveals that TensorFlow and PyTorch excel in high-performance scenarios, while Keras is noted for its accessibility to beginners. The systematic approach taken by Rao—employing both quantitative and qualitative metrics—serves as a model for how image processing libraries can be evaluated on similar dimensions. In the context of this thesis, the criteria used by Rao inform the selection of performance and usability metrics, particularly in environments where both deep learning and traditional image processing techniques may be integrated.
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-
-\subsection{Developments of Computer Vision and Image Processing: Methodologies and Applications (Reis, 2023)}
-
-Reis (2023) offers an editorial overview of recent advances in computer vision and image processing, emphasizing the evolution of methodologies and their application across various domains. This piece underscores the increasing integration of artificial intelligence and deep learning with classical image processing, and it highlights emerging trends that have influenced modern system design.
-
-Reis discusses a range of methodologies—from conventional algorithms to more recent deep learning-based techniques—and illustrates how these approaches are applied in areas such as object detection, segmentation, and quality inspection. Although the article is broad in scope, it provides critical context for the present thesis by outlining both the challenges and opportunities that arise when integrating diverse image processing techniques. The insights provided in this overview underscore the importance of methodological rigor and the need for comprehensive evaluation frameworks that encompass both accuracy and efficiency.
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-
-\subsection{Comparative Literature Review of Machine Learning and Image Processing Techniques for Wood Log Scaling and Grading (Sandvik et al., 2024)}
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-Sandvik et al. (2024) conduct a systematic literature review that compares various machine learning and image processing techniques applied to the scaling and grading of wood logs. This review categorizes studies based on input types, algorithm choices, performance outcomes, and the level of autonomy in industrial applications.
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-The authors highlight a trend towards the increased use of camera-based imaging as opposed to laser scanning, and they emphasize the superior performance of deep learning models in tasks such as log segmentation and grading. While the application domain is specific to wood logs, the review’s methodology—particularly the rigorous categorization and performance comparison—offers a template for evaluating image processing libraries in broader industrial contexts. The challenges identified in comparing heterogeneous approaches, such as varying datasets and evaluation criteria, also reinforce the need for standardized benchmarking protocols, an area that this thesis seeks to address.
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-
-\subsection{The Role of Computer Systems in Comparative Analysis Using Image Processing to Promote Agriculture Business (Sardar, 2012)}
-
-Sardar (2012) explores the application of image processing techniques for quality analysis in the agricultural sector, focusing specifically on the assessment of fruit quality. Although the agricultural context differs from general industrial applications, the underlying principles of computer vision for automated quality control are directly relevant.
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-Sardar’s work describes a system that uses RGB color analysis to grade fruits, highlighting both the strengths and limitations of digital image processing for quality assessment. The paper discusses challenges such as variability in lighting conditions and the need for precise color calibration, issues that are also pertinent in industrial image processing scenarios. By addressing these challenges, Sardar’s study provides valuable lessons on designing robust image processing systems that can maintain accuracy and consistency—an insight that is integrated into the evaluation criteria for image processing libraries in this thesis.
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-
-\subsection{Performance Evaluation of Computer Vision Algorithms on Programmable Logic Controllers (Vieira et al., 2024)}
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-Vieira et al. (2024) examine the feasibility of deploying computer vision algorithms on Programmable Logic Controllers (PLCs), which are widely used in industrial control systems. This study is particularly significant because it evaluates the performance of standard image processing algorithms when executed on hardware platforms with constrained resources.
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-The authors compare the performance of PLC-based image processing with that of traditional computer systems, considering factors such as execution time, implementation complexity, and system robustness. The research identifies trade-offs between simplicity, reliability, and processing power, emphasizing that while PLCs may not offer the same raw performance as high-end computers, they are often sufficient for industrial applications that require tight integration with control systems.
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-This paper is directly relevant to the current thesis, as it informs the discussion on resource efficiency and the practical challenges of implementing image processing libraries in industrial environments. The evaluation criteria developed by Vieira et al.—particularly regarding the balance between processing performance and ease of integration—are mirrored in the present research.
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-
-\subsection{Precision Control of Polyurethane Filament Drafting and Winding Based on Machine Vision (Wu et al., 2022)}
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-Wu et al. (2022) explore the application of machine vision for precision control in the drafting and winding of polyurethane filaments. The study demonstrates how real-time image processing can be integrated into industrial manufacturing processes to enhance control accuracy and product quality.
-
-The authors detail the development of a system that synchronizes machine vision with control mechanisms to monitor and adjust the drafting process in real time. Key performance indicators such as detection accuracy, processing latency, and control responsiveness are evaluated to determine the system’s effectiveness. Wu et al. emphasize the importance of achieving high precision in industrial applications, where even minor deviations can lead to significant defects.
-
-The relevance of this study to the current thesis lies in its demonstration of how image processing libraries can be leveraged to achieve real-time control in manufacturing. The performance metrics and integration challenges discussed in this work provide a benchmark for evaluating similar capabilities in image processing libraries, particularly in terms of their suitability for real-time industrial applications.
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-
-\subsection{A Machine Vision Development Framework for Product Appearance Quality Inspection (Zhu et al., 2022)}
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-Zhu et al. (2022) propose a comprehensive machine vision framework designed for product appearance quality inspection. This study addresses both the algorithmic and system integration aspects of machine vision in industrial settings, emphasizing the need for modular, reusable components that can be easily adapted to various inspection tasks.
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-The framework developed by Zhu et al. incorporates a range of image processing techniques—from basic feature extraction and segmentation to advanced anomaly detection using deep learning. The authors stress that the effectiveness of such systems depends not only on the performance of individual image processing algorithms but also on the overall software architecture, including user interfaces, database management, and input/output communication.
-
-The modular design advocated by Zhu et al. is particularly relevant to the thesis, as it underscores the importance of evaluating image processing libraries not only on their computational performance but also on their ability to integrate into comprehensive industrial systems. The insights from this study inform the criteria for assessing scalability, ease of integration, and overall system robustness in the comparative evaluation conducted in this research.
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-\subsection{Benchmarking Deep Learning for On-Board Space Applications (Ziaja et al., 2021)}
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-Ziaja et al. (2021) focus on benchmarking deep learning algorithms for hardware-constrained environments, such as those used in on-board space applications. While the domain of space imaging differs from industrial applications, the methodological rigor and benchmarking framework presented in this study offer valuable lessons for evaluating image processing libraries.
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-The paper describes a detailed experimental setup in which various deep learning models are benchmarked on standardized datasets, with a focus on metrics such as execution time, resource utilization, and model accuracy. Ziaja et al. emphasize the importance of tailoring performance evaluations to the specific constraints of the hardware, a concept that is directly applicable to industrial image processing where systems often operate under limited computational resources.
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-The study’s approach to parameter tuning, model optimization, and the use of standardized benchmarks provides a robust framework for performance evaluation. These methodologies are particularly useful for the present thesis, which seeks to develop a comprehensive, multidimensional evaluation of image processing libraries based on both performance and resource efficiency. The insights from Ziaja et al. reinforce the necessity of developing configurable benchmarking tools that can accurately capture the trade-offs inherent in deploying image processing systems on various hardware platforms.
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-
-\subsection{Synthesis and Future Directions}
-
-These studies illustrate that the optimal selection of an image processing library is highly context-dependent. For real-time industrial applications, factors such as processing speed, resource efficiency, and ease of integration are paramount. The comparative analyses provided by the reviewed literature underscore that no single library is universally superior; rather, the choice must be informed by specific application requirements and operational constraints.
-
-Several gaps and future research directions have been identified:
-
-\begin{itemize}
-    \item \textbf{Standardization of Benchmarks:} There remains a need for universally accepted benchmarking protocols that enable direct comparisons between different image processing libraries. Future research should focus on developing standardized test suites that account for both performance and resource utilization.
-    \item \textbf{Hybrid and Modular Approaches:} The literature suggests significant potential in combining the strengths of multiple libraries. Investigating hybrid solutions that integrate hardware acceleration with flexible software architectures could yield substantial improvements in industrial applications.
-    \item \textbf{Longitudinal Studies:} Most existing evaluations focus on short-term performance metrics. Long-term studies that assess the stability and scalability of image processing libraries in real-world industrial settings would provide valuable insights for practitioners.
-    \item \textbf{Integration with Emerging Technologies:} As new hardware platforms and acceleration techniques emerge (e.g., GPUs, AI accelerators, and advanced FPGAs), further research is needed to explore how these technologies can be seamlessly integrated with image processing libraries to optimize performance and efficiency.
-\end{itemize}
-
-In summary, the reviewed literature provides a solid foundation for the current thesis. By synthesizing insights from a range of studies, this chapter has contextualized the challenges and opportunities in evaluating image processing libraries for industrial applications. The findings from these works not only inform the performance and resource efficiency criteria used in this thesis but also suggest promising avenues for future research.
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-% References
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