26 Best Books on Computer Vision

“Computer Vision: Algorithms and Applications (Texts in Computer Science)” Book Review: The target audience for this book includes undergraduates, graduates, and research scholars of electrical and electronics, as well as students in embedded systems and computer engineering. Rather than solely presenting the end result, this book takes a step-by-step approach to algorithm development. Additionally, it emphasizes the criticality of algorithm analysis, which involves continuously re-evaluating, modifying, and potentially rejecting algorithms until a satisfactory solution is achieved. The book extensively covers recursion and provides a clear, student-friendly review of its operation and why it is a beneficial programming technique. It utilizes Java-like pseudo code and includes a Java examples appendix.

“Computer Vision Metrics: Survey, Taxonomy, and Analysis” Book Review: This book provides a comprehensive analysis and survey of over 100 historical and current feature description and machine vision methods. It includes a detailed taxonomy for local, regional, and global features. The book delves into the working principles of interest point detectors and feature descriptors, as well as the design process tuning required to achieve invariance targets for specific applications. The taxonomy covers search methods, spectra components, shape, distance functions, accuracy, efficiency, robustness, descriptor representation, and invariance attributes. The book also includes counterpoint discussions to OpenCV community source code resources and provides 540 reference links to enhance understanding of the subject matter. This book is a valuable resource for students and hands-on practitioners seeking to deepen their knowledge in this field.

“Fusion in Computer Vision: Understanding Complex Visual Content” Book Review: This book presents a comprehensive analysis of fusion in computer vision, specifically within the context of international benchmarks that model realistic use cases. It offers a detailed study of late fusion approaches for concept recognition in images and videos. It also evaluates the fusion of multi-modal features of varying semantic levels and their impact on semantic concept detections. Additionally, the book includes models of the human visual system with content understanding methods to help interpret visual content. The proposal for rotation-based ensemble classifiers for high-dimensional data is followed by a discussion on the modeling of mechanisms of human interpretation of complex visual content. Application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies are also explored. This book is an ideal resource for researchers and students seeking to deepen their knowledge in this field.

“Energy Minimization Methods in Computer Vision and Pattern Recognition” Book Review: This book contains 29 carefully selected papers from the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR’97) held in Venice. It provides a detailed exploration of various topics, including Markov random fields, evolutionary search, deterministic methods, structural models, contour and deformable models, and object recognition. In addition to the workshop papers, the book includes four invited papers and a keynote paper authored by top-notch researchers in the field. The book also covers the application of energy minimization techniques in the domains of pattern recognition and compiler vision.

“Computer Vision – ACCV 2007” Book Review: This book comprises the refereed proceedings of the 8th Asian Conference on Computer Vision, ACCV 2007. It begins by introducing the fundamentals of shape and texture, image and video processing, face and gesture recognition, tracking, camera networks, learning, motion and tracking, and retrieval and search. The subsequent chapters delve into more advanced topics such as human pose estimation, matching, face/gesture/action detection and recognition, low-level vision and photometry, motion and tracking, human detection, and segmentation. This book is an ideal resource for both students and professionals seeking to enhance their knowledge in this field.

“Embedded Computer Vision” Book Review: This book provides a comprehensive perspective on the ever-evolving techniques in embedded computer vision, recent advances in the field, and its future potential in research and practical applications. The book covers hardware, architectures, design methodologies, and video analytics, demonstrating the advantages of various architectures, high-level software framework development, and cost-effective algorithmic alternatives. It uses the example of an automotive safety application to assess the challenges associated with fixed-point processor implementation. Additionally, the book addresses issues pertaining to security, connectivity, safety, health, energy efficiency, and more, while examining trade-offs for designing cost-effective systems. This book is a valuable resource for researchers, practitioners, and graduate students seeking to enhance their knowledge in this field.

“Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding” Book Review: This book provides an in-depth look into the various approaches that have been researched in computer vision for action recognition. Through a step-by-step methodology, readers can gain an understanding of how computer vision can be utilized to recognize various human actions. The book is suitable for students and researchers who have prior knowledge of the fundamentals of image processing.

“Dictionary of Computer Vision and Image Processing” Book Review: This book is a complete guide to the commonly used terminology in computer vision, image processing, machine vision, and other related fields. It also includes recent concepts and terms, along with reference links for further clarification. With concise definitions for over 3500 terms, mathematical proofs, and practical examples where applicable, the book designed to the needs of experienced professionals, researchers, as well as senior undergraduate and graduate-level students.

“Automatic Speech Recognition on Mobile Devices and over Communication Networks (Advances in Computer Vision and Pattern Recognition)” Book Review: This book covers the increasing demand for automatic speech recognition in mobile devices and communication networks, driven by the advancements in computing and networking technologies. By bringing together academic researchers and industry experts, it provides a comprehensive introduction to speech recognition in devices and networks. The book covers topics such as network, distributed and embedded speech recognition systems.

“Markov Models for Pattern Recognition: From Theory to Applications (Advances in Computer Vision and Pattern Recognition)” Book Review: The new edition of this book has been extensively updated and expanded to include a more detailed treatment of the EM algorithm, an efficient approximate Viterbi training method, a theoretical analysis of the perplexity measure, and coverage of multi-pass decoding based on n-best search. While emphasizing the theoretical foundations of Markov modeling, this book also places significant emphasis on practical algorithmic solutions. Key features include: presentation of the conventional framework for Markov models, coverage of robust treatment of probability quantities, presentation of techniques for the development of hidden Markov models for specific application areas, description of important methods for efficient training of Markov models and their conversion to different tasks, examination of algorithms for searching within the complex solution spaces resulting from the joint use of Markov chain and hidden Markov models, and review of key applications of Markov models.

“Video Text Detection (Advances in Computer Vision and Pattern Recognition)” Book Review:
This book provides a concise introduction to the latest developments in video text detection. Starting with the fundamental theory and a brief history of video text detection, the text covers pre-processing and post-processing techniques, character segmentation and recognition, identification of non-English scripts, methods for multi-modal analysis, and performance evaluation. The book examines text detection from both natural video scenes and artificially embedded subtitles. Additionally, it evaluates various applications of the technology, from license plate recognition and road navigation assistance to sports analysis and video advertising systems. Highlights include a concise explanation of the fundamental theory, supplemented with references for further reading, practical methods to help the reader understand and develop their own video text detection systems and applications, and an easy-to-navigate reference, presenting the material in separate chapters.

“Computer Vision for Electronics Manufacturing (Advances in Computer Vision and Machine Intelligence)” Book Review: This book addresses the challenges of processing and interpreting massive amounts of sensory data. It is divided into two parts, covering both application and system aspects, as well as vision algorithms for electronics manufacturing. The first section discusses components of vision systems, including imaging microscopes for microelectronics and meteorology in electronic devices and substrates, as well as various inspection, repair, and testing techniques. The second section explores different types of vision algorithms, such as logic, detection, and geometric correction.

“Pyramidal Architectures for Computer Vision (Advances in Computer Vision and Machine Intelligence)” Book Review: This book provides guidance on selecting the optimal resolution for specific operations, tasks, and images, as well as an overview of hierarchical architectures and their use in computer vision systems. It covers both homogeneous and heterogeneous hierarchical topologies, and delves into the design of pipeline multiresolution systems and the simulation of pyramids on flat arrays and hypercubes. In addition, it explores the use of pyramidal tools and their applications.

“Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications (Advances in Computer Vision and Pattern Recognition)” Book Review: This book serves as a reference for undergraduates, graduates, and research scholars in the fields of electrical and electronics engineering, embedded systems, and computer engineering. It is also useful for graduate students studying computer vision, pattern recognition, and multimedia. The book explores the theoretical foundations and algorithm implementation details of compressed sensing, focusing on its practical application in visual recognition and computer vision. It covers sparse recovery methods, robust and efficient sparse representation, and large-scale visual recognition. Additionally, it discusses feature representation and learning, sparsity-induced similarity, and sparse representation-based classifiers, as well as low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning. The book also includes appendices that provide additional computer programming resources.

“Statistical Learning and Pattern Analysis for Image and Video Processing (Advances in Computer Vision and Pattern Recognition)” Book Review: This book is designed for the students, researchers, and scientists who are interested in image processing topics such as image segmentation, stereo matching, object detection and analysis, and visual tracking. It delves into harmonic analysis and partial differential equations as well as their practical applications in video processing. Each chapter concludes with examples, exercises, and questions to aid readers in comprehending the material.

“Feature Extraction and Image Processing for Computer Vision” Book Review: This book is a comprehensive guide to the techniques of image processing and computer vision. It includes sample Matlab codes to help readers understand and implement the algorithms. The book covers various topics such as feature extraction, Haar wavelets, Viola-Jones, bilateral filtering, and SURF. It also discusses object detection, tracking, symmetry operation, LBP textures, and AdaBoost. Additionally, the book provides information on distance measures and level sets. The content is presented in a clear and concise manner, making it suitable for engineers and students in this field.

“Image Analysis and Recognition” Book Review: This book presents an introduction to image analysis, pattern recognition, and the latest advances in theories and methodologies, including topics on machine intelligence. It is a collection of proceedings from the 13th conference on image analysis, which covers pattern recognition, image assessment, feature extraction, and detection. The book also discusses various applications of RGB-D cameras, 3D computers, biometrics, brain imaging, and ophthalmology. It further focuses on cardiovascular images, obituaries, and biomedical imaging. This book is a valuable resource for researchers and students in the field of image analysis and pattern recognition.

“Image Registration: Principles, Tools and Methods (Advances in Computer Vision and Pattern Recognition)” Book Review: This book is a comprehensive guide to image registration, covering principles, tools, and methods. It explains the various components of image registration and their respective designs for analysis tools. It includes discussion of similarity and dissimilarity measures, point detectors, heterogeneous descriptors, and robust estimators. The book covers topics such as image resampling, principle axes, optimization, and model-based methods of transformation. Additionally, an appendix on PCA is included, along with other glossaries throughout the book. This resource is a valuable reference for anyone interested in image registration.

“Computer Vision: Models, Learning, and Inference” Book Review: This book is aimed at advanced undergraduate and graduate students and practitioners in computer vision. It presents a contemporary approach to computer vision that emphasizes learning and inference in probabilistic models as a common thread. The book demonstrates how to leverage training data to understand the connections between the observed image data and the aspects of the world we want to estimate, such as object class or 3D structure. It also illustrates how to utilize these relationships to make novel inferences about the world from new image data. Beginning with the fundamentals of probability and model fitting, the book gradually progresses to real-world examples.

“OpenCV: Computer Vision Projects with Python” Book Review: This book is intended for students who possess a working understanding of Python and are interested in experimenting with OpenCV. It guides readers through the installation of OpenCV and related software, such as Python, NumPy, SciPy, OpenNI, and SensorKinect, on Windows, Mac, or Ubuntu. Additionally, the book demonstrates how to execute geometric transformations on images, apply image filtering techniques, and transform an image into a cartoon-like representation. Subsequently, the book provides more in-depth coverage of geometric transformations, image filtering, and cartoonization techniques.

“Multimedia Interaction and Intelligent User Interfaces: Principles, Methods and Applications (Advances in Computer Vision and Pattern Recognition)” Book Review: This book comprehensively presents the latest advancements in the applications of multimedia interaction and user interfaces for consumer electronics. It covers topics related to multimedia content analysis and human-machine interaction, including novel and computationally efficient algorithms for extracting semantically meaningful audio-visual events. The book also addresses the cognitive impacts of modality on human information processing and provides an overview of gesture control technologies for CE. It will be valuable for researchers and practitioners in the field of multimedia analysis, human-computer interaction, and interactive user interfaces, as well as graduate students studying computer vision and pattern recognition.

“Visual Attributes” Book Review: This book offers a comprehensive overview of the latest developments in machine learning and computer vision, with a focus on visual attributes. It explores the interdisciplinary nature of this field, including its intersection with computational linguistics and human-machine interaction. The book covers various topics such as zero-shot classification, learning with privileged information, and multi-task attribute learning based on attribute-based methods. It also delves into relative attributes and their effectiveness in image search applications, reviews the state-of-the-art in estimating human attributes, and discusses their use in various applications. The book also discusses attempts to build a vocabulary of visual attributes and explores the connections between visual attributes and natural language. Contributions come from renowned scientists from around the world, providing both theoretical insights and practical applications. This book is a valuable resource for researchers and practitioners in the field of computer vision, machine learning, and related disciplines.

“Deep Structure, Singularities, and Computer Vision: First International Workshop” Book Review: The book talks about the difficulty in understanding the suggestions related to the metric properties of a sign and the places of its inborn construction. The book also highlights the generalization of the outcome to scientific signs and zero intersections of the Laplacean. However, the remaining part of the book is an open issue. The book Deep Structure, Singularities, and Computer Vision covers topics related to getting information about what information is actually carried by the singularities of shapes and dim scale pictures. The book covers testing of the metric data that is encoded in the design of shapes and pictures.

“Programming Computer Vision with Python: Tools and algorithms for analyzing images” Book Review: This book provides a comprehensive introduction to the tools and algorithms used in image analysis, covering various computer vision applications such as object recognition, 3D reconstruction, stereo imaging, and augmented reality. It presents the underlying theory of vision in a clear and accessible manner, with code samples and explanations to facilitate reproducibility and practice. The book is suitable for students, researchers, and enthusiasts with basic programming and mathematical skills, as it covers techniques used in diverse fields such as robot navigation, medical image analysis, and image mappings and transforms like texture warping and panorama creation. The book also discusses in detail 3D reconstructions from multiple images of a scene, image retrieval techniques based on visual content, and algorithms for image classification and object recognition.

“Computer Vision, Models And Inspection (World Scientific Series In Robotics And Intelligent Systems)” Book Review: The focus of this book is on computer vision and model-based matching, making it an excellent introductory course in computer vision. The authors delve into various topics such as 2D and 3D block matching, image acquisition, image preprocessing, segmentation, feature representation, and reasoning. In addition, the book also covers model-based matching, geometric modeling, practical model-based matching, inspection, geometric tolerances, and inspection. This comprehensive resource is ideal for individuals who are taking their first steps in the field of computer vision.

“Computer Vision in Control Systems-3: Aerial and Satellite Image Processing (Intelligent Systems Reference Library)” Book Review: The book presents the latest developments in computer vision techniques and solutions, with a particular focus on aerial and satellite image processing. It includes a selection of contributions covering topics such as novel tensor analysis, comparative morphology schemes, and warping compensation for video stabilization.