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4 books on Computer Vision [PDF]
Updated: March 07, 2024 | 23 |
Books on computer vision are foundational resources for startups focusing on computer vision technology. These texts provide an in-depth understanding of the principles, algorithms, and techniques essential for image and video analysis, object recognition, and visual understanding.
1. Computer Vision: Algorithms and Applications
2023 by Richard Szeliski
"Computer Vision: Algorithms and Applications" delves into a diverse array of methods commonly employed for the analysis and interpretation of images. It not only delves into the practical techniques but also delves into the intriguing real-world applications where computer vision has proven to be remarkably effective, ranging from specialized fields like medical imaging to enjoyable consumer-level tasks such as image enhancement and stitching—applications that students can directly apply to their personal photos and videos. This authoritative and all-encompassing textbook and reference text goes beyond offering mere "recipes," adopting a scientific approach to fundamental vision challenges by establishing physical models of the imaging process and subsequently utilizing them to generate scene descriptions. These issues are further examined using statistical models and rigorously addressed through engineering methodologies.
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2. Practical Machine Learning for Computer Vision
2021 by Valliappa Lakshmanan, Martin Görner, Ryan Gillard
"Practical Machine Learning for Computer Vision" is a hands-on guide that demonstrates the utilization of machine learning models to extract valuable insights from images. Aimed at machine learning engineers and data scientists, the book provides practical solutions to a diverse range of image-related challenges, spanning classification, object detection, autoencoders, image generation, counting, and captioning, employing tried-and-true machine learning methodologies. The comprehensive journey into the realm of deep learning encompasses every facet, from creating datasets and data preprocessing to model architecture design, training, assessment, deployment, and interpretability. Co-authored by Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard, this book equips readers with the expertise to develop precise and interpretable computer vision machine learning models, seamlessly integrating them into large-scale production through a robust machine learning infrastructure, all while ensuring flexibility and ease of maintenance. The book empowers readers to craft, train, evaluate, and make predictions using models built in TensorFlow or Keras.
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3. Computer Vision Metrics: Survey, Taxonomy, and Analysis
2014 by Scott Krig
"Computer Vision Metrics: A Comprehensive Examination, Classification, and Evaluation" offers a comprehensive review and in-depth analysis of over 100 contemporary and historical methods for feature description and machine vision. It introduces a meticulous taxonomy encompassing local, regional, and global features, supplying essential context to cultivate an understanding of the underlying principles behind the efficacy and design of interest point detectors and feature descriptors. The book imparts valuable insights into the process of fine-tuning these methods to attain specific robustness and invariance objectives tailored to particular applications. While it provides a broad survey, offering over 540 references for further exploration, the taxonomy spans an array of aspects, including search techniques, spectral components, descriptor representation, shape analysis, distance metrics, accuracy, efficiency, robustness, and invariance characteristics, among others. In contrast to furnishing step-by-step source code and shortcuts, this book serves as a thought-provoking companion, fostering a deeper understanding alongside the extensive resources available in the OpenCV community for practical application by practitioners.
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4. Computer Vision: Models, Learning, and Inference
2012 by Simon J. D. Prince
"Computer Vision: Models, Learning, and Inference" offers a contemporary perspective on computer vision, centering on the overarching theme of learning and inference within probabilistic models. The book elucidates how training data can be harnessed to discern the intricate relationships between observed image data and the facets of the world we seek to deduce, whether it's the 3D structure or object classification. It demonstrates how these insights can be leveraged to draw new inferences from fresh image data. Starting with foundational concepts of probability and model fitting, the book progressively advances to real-world examples that readers can implement and adapt to construct practical vision systems. Designed primarily for advanced undergraduates and graduate students, the comprehensive methodology presented herein also proves valuable to computer vision practitioners. The book encompasses state-of-the-art techniques, including graph cuts, machine learning, and multiple view geometry, offering a unified approach that underscores the common ground among solutions to pivotal computer vision challenges such as camera calibration, face recognition, and object tracking. Detailed descriptions of over 70 algorithms are provided, with ample illustrations enhancing the text, and the self-contained treatment encompasses all the necessary background mathematics. Supplementary resources are available at www.computervisionmodels.com.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Computer Vision: Algorithms and Applications
2023 by Richard Szeliski
"Computer Vision: Algorithms and Applications" delves into a diverse array of methods commonly employed for the analysis and interpretation of images. It not only delves into the practical techniques but also delves into the intriguing real-world applications where computer vision has proven to be remarkably effective, ranging from specialized fields like medical imaging to enjoyable consumer-level tasks such as image enhancement and stitching—applications that students can directly apply to their personal photos and videos. This authoritative and all-encompassing textbook and reference text goes beyond offering mere "recipes," adopting a scientific approach to fundamental vision challenges by establishing physical models of the imaging process and subsequently utilizing them to generate scene descriptions. These issues are further examined using statistical models and rigorously addressed through engineering methodologies.
Download PDF
2. Practical Machine Learning for Computer Vision
2021 by Valliappa Lakshmanan, Martin Görner, Ryan Gillard
"Practical Machine Learning for Computer Vision" is a hands-on guide that demonstrates the utilization of machine learning models to extract valuable insights from images. Aimed at machine learning engineers and data scientists, the book provides practical solutions to a diverse range of image-related challenges, spanning classification, object detection, autoencoders, image generation, counting, and captioning, employing tried-and-true machine learning methodologies. The comprehensive journey into the realm of deep learning encompasses every facet, from creating datasets and data preprocessing to model architecture design, training, assessment, deployment, and interpretability. Co-authored by Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard, this book equips readers with the expertise to develop precise and interpretable computer vision machine learning models, seamlessly integrating them into large-scale production through a robust machine learning infrastructure, all while ensuring flexibility and ease of maintenance. The book empowers readers to craft, train, evaluate, and make predictions using models built in TensorFlow or Keras.
Download PDF
3. Computer Vision Metrics: Survey, Taxonomy, and Analysis
2014 by Scott Krig
"Computer Vision Metrics: A Comprehensive Examination, Classification, and Evaluation" offers a comprehensive review and in-depth analysis of over 100 contemporary and historical methods for feature description and machine vision. It introduces a meticulous taxonomy encompassing local, regional, and global features, supplying essential context to cultivate an understanding of the underlying principles behind the efficacy and design of interest point detectors and feature descriptors. The book imparts valuable insights into the process of fine-tuning these methods to attain specific robustness and invariance objectives tailored to particular applications. While it provides a broad survey, offering over 540 references for further exploration, the taxonomy spans an array of aspects, including search techniques, spectral components, descriptor representation, shape analysis, distance metrics, accuracy, efficiency, robustness, and invariance characteristics, among others. In contrast to furnishing step-by-step source code and shortcuts, this book serves as a thought-provoking companion, fostering a deeper understanding alongside the extensive resources available in the OpenCV community for practical application by practitioners.
Download PDF
4. Computer Vision: Models, Learning, and Inference
2012 by Simon J. D. Prince
"Computer Vision: Models, Learning, and Inference" offers a contemporary perspective on computer vision, centering on the overarching theme of learning and inference within probabilistic models. The book elucidates how training data can be harnessed to discern the intricate relationships between observed image data and the facets of the world we seek to deduce, whether it's the 3D structure or object classification. It demonstrates how these insights can be leveraged to draw new inferences from fresh image data. Starting with foundational concepts of probability and model fitting, the book progressively advances to real-world examples that readers can implement and adapt to construct practical vision systems. Designed primarily for advanced undergraduates and graduate students, the comprehensive methodology presented herein also proves valuable to computer vision practitioners. The book encompasses state-of-the-art techniques, including graph cuts, machine learning, and multiple view geometry, offering a unified approach that underscores the common ground among solutions to pivotal computer vision challenges such as camera calibration, face recognition, and object tracking. Detailed descriptions of over 70 algorithms are provided, with ample illustrations enhancing the text, and the self-contained treatment encompasses all the necessary background mathematics. Supplementary resources are available at www.computervisionmodels.com.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
