Certainly. Here are three highly regarded books on computer vision, each catering to different aspects of the field:
1. Computer Vision: Algorithms and Applications by Richard Szeliski This book is a foundational resource for understanding both classical and modern computer vision techniques. Szeliski, a researcher with extensive industry and academic experience, organizes the material to balance theory with practical implementation. The text covers core topics like image formation, feature detection, segmentation, and 3D reconstruction, while also addressing advanced areas such as deep learning-based object recognition. For example, the chapter on image stitching explains geometric transformations and blending algorithms used in tools like panorama apps. The book includes mathematical derivations but emphasizes intuitive explanations, making it accessible to developers with a basic linear algebra and calculus background. It’s particularly useful for engineers who want to build systems from scratch or adapt existing algorithms.
2. Deep Learning for Computer Vision by Rajalingappaa Shanmugamani Focusing on neural networks and their application to vision tasks, this book bridges theory and code. Shanmugamani provides clear examples using frameworks like TensorFlow and Keras, covering convolutional networks (CNNs), transfer learning, and generative models. A standout feature is the practical guidance on training models for tasks such as image classification (e.g., ResNet), object detection (e.g., YOLO), and image generation (e.g., GANs). The book also addresses deployment challenges, including optimizing models for mobile devices using TensorFlow Lite. Developers working on AI-powered applications—such as medical imaging or autonomous vehicles—will appreciate the code snippets and troubleshooting tips for common issues like overfitting or data augmentation.
3. Learning OpenCV 4 by Gary Bradski and Adrian Kaehler For hands-on developers, this book is a guide to using OpenCV, the widely adopted open-source library. Updated for OpenCV 4, it walks through real-world projects like facial recognition, augmented reality, and video analysis. The authors explain core functions (e.g., edge detection with Canny filters) and newer modules, such as deep learning integration with DNNs. For instance, the chapter on camera calibration demonstrates how to correct lens distortion for robotics applications. Code examples in Python and C++ help readers implement features efficiently, whether for prototyping or production. This book is ideal for engineers building vision systems that require low-level optimization or integration with hardware like cameras or drones.
Each of these books addresses a distinct need: Szeliski’s for theory, Shanmugamani’s for deep learning workflows, and Bradski/Kaehler’s for OpenCV mastery. Together, they provide a well-rounded toolkit for developers tackling vision projects.