The Best New Computer Vision Books To Read In 2025

Updated for OpenCV 5, this book covers the latest on depth cameras, 3D navigation, deep neural networks, and Cloud computing, helping you solve real-world computer vision problems with practical code. Key FeaturesBuild powerful computer vision applications in concise code with OpenCV 5 and Python 3Learn the fundamental concepts of image processing, object classification, and 2D and 3D trackingTrain, use, and understand machine learning models, and deploy them in the CloudBook DescriptionComputer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases and techniques.

This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, depth estimation, and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects. What you will learnInstall and familiarize yourself with OpenCV 5's Python 3 bindingsUnderstand image processing and video analysisUse a depth camera to distinguish foreground and background regionsDetect and identify objects, and track their motion in videosTrain and use your own models to match images and classify objectsDetect and recognize faces, and classify their gender and ageBuild augmented reality applications, and navigate the real 3D worldTrain neural networks and deploy them as Cloud-based solutionsWho This Book Is ForThis OpenCV book is a good fit for Python programmers who want to get started with computer vision and machine learning. This book will also be useful for Computer vision and AI/ML developers who want to expand their OpenCV skills as well as experts who want to stay up-to-date with OpenCV 5. Table of ContentsSetting Up OpenCVHandling Files, Cameras, and GUIsProcessing Images with OpenCVDepth Estimation and SegmentationDetecting and Recognizing FacesRetrieving Images and Searching Using Image DescriptorsBuilding Custom Object DetectorTracking ObjectsCamera Models and Augmented Reality3D Reconstruction and NavigationNeuraNetworks with OpenCV - an IntroductionOpenCV Applications at Scale.

About the Technology:. Transformers, initially designed for natural language processing tasks, have rapidly evolved into a powerhouse for computer vision applications. Known for their self-attention mechanisms, these models excel in tasks that require the analysis of complex relationships within data. In "Transformers for Computer Vision," we delve into how these innovative architectures are adapted to revolutionize image classification, object detection, and image segmentation.

Authored by a leading expert in the field of artificial intelligence, this book provides an authoritative look at the application of transformer architectures within the realm of computer vision. It is underpinned by the latest research and case studies from real-world implementations, ensuring that the reader receives the most current and practical information. Summary of the Book:. "Transformers for Computer Vision" explores the cutting-edge application of transformer models in handling visual data. This book begins with a foundational introduction to transformer technology, followed by detailed explorations of how these models can be optimized and implemented for tasks such as image classification, object detection, and segmentation. Through a blend of theoretical insights and practical implementation tips, the book demystifies the complexities of these models for real-world applications. What's Inside:. - Detailed explanations of transformer technology and its adaptation for visual tasks. - Practical guidance on integrating transformers with existing computer vision architectures. - Step-by-step tutorials on building transformer-based models for various imaging tasks. - In-depth case studies illustrating the effectiveness of transformers in real-world scenarios. - Advanced topics on improving the efficiency and performance of vision transformers. About the Reader:. This book is ideal for AI practitioners, machine learning engineers, and researchers looking to deepen their understanding of transformer architectures in the field of computer vision. It is also suitable for advanced students in computer science and related fields who are interested in the latest developments in AI technology. In the fast-evolving field of artificial intelligence, staying current with the latest technologies like transformers is crucial. This book provides a timely exploration into one of the most significant advancements in AI, helping readers not just catch up but stay ahead in the ever-changing landscape of computer vision. Unlock the potential of Vision Transformers in your projects today! Grab your copy of "Transformers for Computer Vision" and start transforming your approach to visual data with cutting-edge AI technology. Whether you're refining technical skills or spearheading innovative projects, this book is your gateway to mastering transformer models in computer vision. [Purchase your copy now!].

Step into the fascinating world of visual artificial intelligence with "Introduction to Computer Vision with TensorFlow." This comprehensive guide is perfect for beginners eager to explore the rapidly evolving field of computer vision, a domain that enables machines to interpret and make decisions based on visual data. From understanding the core concepts of computer vision to mastering the advanced techniques of deep learning, this book covers it all. Discover the foundational principles of how computers process and analyze images and videos, drawing meaningful insights just like the human brain.

Learn about convolutional neural networks (CNNs), data preprocessing, image augmentation, and key algorithms for object detection, recognition, and image segmentation. With hands-on tutorials and real-world examples, you'll gain practical experience building computer vision models using TensorFlow, a powerful open-source framework by Google. Whether you are aiming to develop healthcare imaging solutions, create autonomous driving software, or innovate in retail analytics, this book provides the tools and knowledge you need to succeed. Key topics covered:. Understanding Computer Vision: Grasp the essentials of visual data interpretation and explore the role of artificial intelligence in enhancing computer vision.Getting Started with TensorFlow: Learn how to install, set up, and use TensorFlow for building robust computer vision models.Core Computer Vision Tasks: Delve into image classification, object detection, image segmentation, and more with step-by-step guides.Advanced Techniques and Models: Master advanced concepts like transfer learning, facial recognition, generative adversarial networks (GANs), and 3D computer vision.Real-World Applications and Case Studies: Explore practical applications across various industries, from healthcare to automotive, and learn from real-world case studies that demonstrate the power of visual AI.Performance Optimization and Deployment: Learn best practices for optimizing your models for deployment on edge devices and in cloud environments.Perfect for students, researchers, and developers new to the field, "Introduction to Computer Vision with TensorFlow" offers a solid foundation and hands-on experience to build your skills in visual AI. Start your journey today and unlock the potential of computer vision to revolutionize technology and transform industries.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security.

The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;methods capable of retrieving photometric and geometric transformed images;concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;a comprehensive study of content-based image-retrieval techniques for feature extraction;machine learning approaches to understanding angiogenesis;handwritten image enhancement based on neutroscopic-fuzzy. Audience. The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field.

It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features. - Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models. - Build solutions for real-world computer vision problems using PyTorch. - All the code files are available on GitHub and can be run on Google Colab. Book Description.

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems. What you will learn. - Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer. - Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks. - Implement multi-object detection and segmentation. - Leverage foundation models to perform object detection and segmentation without any training data points. - Learn best practices for moving a model to production. Who this book is for. This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book. Table of Contents. - Artificial Neural Network Fundamentals. - PyTorch Fundamentals. - Building a Deep Neural Network with PyTorch. - Introducing Convolutional Neural Networks. - Transfer Learning for Image Classification. - Practical Aspects of Image Classification. - Basics of Object Detection. - Advanced Object Detection. - Image Segmentation. - Applications of Object Detection and Segmentation. - Autoencoders and Image Manipulation. - Image Generation Using GANs. (N.B. Please use the Read Sample option to see further chapters).

In the realm of computer vision, understanding the basics is crucial for delving into more advanced concepts and applications. Computer vision is a field that focuses on enabling computers to interpret and understand visual information from the world around them. It involves processing and analyzing digital images and videos to extract meaningful insights and make informed decisions.

This subchapter will provide a comprehensive overview of the fundamentals of computer vision, setting the foundation for mastering more complex topics such as Convolutional Neural Networks (CNNs), Deepfakes, Generative Adversarial Networks (GANs), Biometrics, and AI Security. At its core, computer vision relies on algorithms and mathematical techniques to extract features and patterns from visual data. These algorithms allow computers to recognize objects, detect motion, track objects, and perform other tasks that mimic human visual perception. By understanding the basics of computer vision, individuals can gain a deeper appreciation for the technology behind applications such as facial recognition, autonomous vehicles, medical imaging, and augmented reality. One key concept in computer vision is image preprocessing, which involves preparing raw images for analysis by enhancing their quality, removing noise, and adjusting their contrast and brightness. Image preprocessing plays a critical role in improving the accuracy and performance of computer vision algorithms. Other fundamental concepts include image segmentation, object detection, image classification, and image recognition, which form the building blocks of more advanced computer vision techniques. In addition to understanding the technical aspects of computer vision, it is essential to consider the ethical and security implications of this technology. With the rise of deepfakes, which are digitally manipulated videos or images that can deceive viewers, ensuring the authenticity and integrity of visual data has become a pressing concern. Biometric systems, which use unique physical characteristics such as fingerprints or facial features for identification, also raise privacy and security issues that must be carefully addressed. As individuals dive deeper into the world of computer vision, they will encounter challenges related to data quality, model training, performance evaluation, and ethical considerations. By mastering the basics of computer vision and staying informed about the latest advancements in the field, individuals can harness the power of this technology to drive innovation, enhance security, and improve the way we interact with the visual world.

This book is a valuable resource for professionals, researchers, and students who want to expand their knowledge of advanced computer vision techniques using PyTorch.

This book offers a comprehensive and unbiased introduction to 3D Computer Vision, ranging from its foundations and essential principles to advanced methodologies and technologies.

Divided into 11 chapters, it covers the main workflow of 3D computer vision as follows: camera imaging and calibration models; various modes and means of 3D image acquisition; binocular, trinocular and multi-ocular stereo vision matching techniques; monocular single-image and multi-image scene restoration methods; point cloud data processing and modeling; simultaneous location and mapping; generalized image and scene matching; and understanding spatial-temporal behavior. Each topic is addressed in a uniform manner: the dedicated chapter first covers the essential concepts and basic principles before presenting a selection of typical, specific methods and practical techniques. In turn, it introduces readers to the most important recent developments, especially in the last three years. This approachallows them to quickly familiarize themselves with the subject, implement the techniques discussed, and design or improve their own methods for specific applications. The book can be used as a textbook for graduate courses in computer science, computer engineering, electrical engineering, data science, and related subjects. It also offers a valuable reference guide for researchers and practitioners alike.

This comprehensive textbook presents a broad review of both traditional (i.e., conventional) and deep learning aspects of object detection in various adversarial real-world conditions in a clear, insightful, and highly comprehensive style. Beginning with the relation of computer vision and object detection, the text covers the various representation of. objects, applications of object detection, and real-world challenges faced by the research community for object detection task.

The book addresses various real-world degradations and artifacts for the object detection task and also highlights the impacts of artifacts in the object detection problems. The book covers various imaging modalities and benchmark datasets mostly adopted by the research community for solving various aspects of object detection tasks. The book also collects together solutions and perspectives proposed by the preeminent researchers in the field, addressing not only the background of visibility enhancement but also techniques proposed in the literature for visibility enhancement of scenes and detection of objects in various representative real-world challenges. Computer Vision: Object Detection in Adversarial Vision is unique for its diverse content, clear presentation, and overall completeness. It provides a clear, practical, and detailed introduction and advancement of object detection in various representative challenging real-world conditions. Topics and Features:. • Offers the first truly comprehensive presentation of aspects of the object detection in degraded and nondegraded environment. • Includes in-depth discussion of various degradation and artifacts, and impact of those artifacts in the real world on solving the object detection problems. • Gives detailed visual examples of applications of object detection in the real world. • Presents a detailed description of popular imaging modalities for object detection adopted by researchers. • Presents the key characteristics of various benchmark datasets in indoor and outdoor environment for solving object detection tasks. • Surveys the complete field of visibility enhancement of degraded scenes, including conventional methods designed for enhancing the degraded scenes as well as the deep architectures. • Discusses techniques for detection of objects in real-world applications. • Contains various hands-on practical examples and a tutorial for solving object detection problems using Python. • Motivates readers to build vision-based systems for solving object detection problems in degraded and nondegraded real-world challenges. The book will be of great interest to a broad audience ranging from researchers and practitioners to graduate and postgraduate students involved in computer vision tasks with respect to object detection in degraded and nondegraded real-world vision problems.