Add: bib files

sources/bibs/1.1.1.bib

+
+@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},
+}

sources/bibs/1.1.2.bib

+
+@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},
+}

sources/bibs/1.1.3.bib

+
+@misc{bradski_opencv_nodate,
+	title = {The {OpenCV} {Library}},
+	url = {http://www.drdobbs.com/open-source/the-opencv-library/184404319},
+	abstract = {OpenCV is an open-source, computer-vision library for extracting and processing meaningful data from images.},
+	urldate = {2025-03-23},
+	journal = {Dr. Dobb's},
+	author = {Bradski, Gary},
+	file = {Snapshot:C\:\\Users\\SFI19\\Zotero\\storage\\A9RVSN8V\\184404319.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{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},
+}
+
+@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},
+}

sources/bibs/1.1.bib

+
+@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},
+}