Computer vision is known as an interdisciplinary computer science field that deals with how systems can acquire a high-level grasp from digital images and videos. When Computers have started understanding images and works on validation towards visualization, recognition, and processing of images.
Computer Vision is one of the most trending subfields of artificial intelligence that has a large number of uses in the practical industry. If you are starting to learn this subject, you can read the best computer vision books, we have compiled here for learning, exploring this topic, and growing your career. The books listed here contain many theoretical concepts and practical, concentrating on the mathematics behind the computer vision field.
Computer Vision Books List:
1. Computer Vision: Models, Learning, And Inference
A book by writer Simon J. D. Prince. This latest treatment of computer vision concentrates on learning as well as reasoning in probabilistic models as a unification theme. It displays the perfect usage of training information to learn the relationships between the distinguished image data and the aspects of the things or types of information that we have selected to estimate, like the 3D structure or simply the object class, and the exact way to cash in these relationships to ultimately make new inferences related to the world from new image data.
With the requirement of nominal prerequisites, this book begins with the basics of probability and model fitting. It also works up towards real examples that all the readers can implement and customize to develop functional vision systems. This book is very useful for computer vision enthusiasts and practitioners. There are more than 70 algorithms described in the required details for implementation. Also, it’s a useful book that covers groundbreaking techniques including machine learning and multiple view geometry.
2. Deep Learning For Vision Systems
The author of this book is Mohamed Elgendy, who is a well-known researcher. This book provides information on how to apply deep learning and computer vision to make the computer understand what it sees. You will get to know the understandings of how to utilize deep learning architectures to develop vision system applications for facial recognition and image modification as well as generation. This book provides detailed information on the below-mentioned topics-
– Image classification techniques and object detection
– Advanced level deep learning architectures
– Transfer learning and generative adversarial networks
– DeepDream and neural style transfer
– Visual embeddings and Image search process
3. Computer Vision: Algorithms And Applications
This book explores the different types of techniques generally used to analyze, learn and interpret images. It also details difficult and real-world applications where vision is successfully used, both for special applications like medical imaging and for general, consumer-centric tasks such as image editing and producing, that is mostly used by students for their personal photos and videos.
This book is more than just a source of “recipes,” and contains extraordinarily authoritative and complete textbook/reference information and also takes a focused technical approach towards all the basic vision problems, arranging together physical models of the complete imaging procedure before inverting them to producing the descriptions of a particular scene. All These problems and issues are also analyzed utilizing statistical models and generally solved using thorough engineering techniques.
4. Modern Computer Vision with PyTorch
The two authors of this book are V Kishore Ayyadevara and Yeshwanth Reddy. This textbook provides a hands-on approach to assist you to solve over 50 Computer Vision problems utilizing PyTorch1.x on real-life datasets. Also, this book particularly focuses on beginners in Pytorch and also intermediate machine learning professionals who look to develop some expertise in computer vision techniques with the help of deep learning and PyTorch. You will learn how to train Neural Networks from scratch by using NumPy and PyTorch.
You’ll also learn about image classification utilizing convolutional neural networks and also transfer learning and understanding the functioning. As you start learning further, you’ll implement more than one use case of 2D as well as 3D multi-object detection, segmentation, human-pose-estimation with regards to learning about R-CNN family, SSD, YOLO, U-Net architectures, and the famous Detectron2 platform.
5. Multiple View Geometry in Computer Vision
The authors of this book Richard Hartley and Andrew Zisserman have greatly described the most important techniques in established multiple view geometry, both traditional and modern, in an understandable and consistent way. A common issue in computer vision is to understand the structure of a real-world scene. This book covers the major geometric principles and tells about the way to defines objects in the algebraic form to make it easy for computation and application. Current crucial developments in the theoretical and practical scene reconstruction are mentioned in-depth in a unified framework.
6. Learning OpenCV 4 Computer Vision with Python 3
Joseph Howse is the author of this book who has updated this book for OpenCV 4 and Python 3. This book covers the latest in-depth cameras, 3D tracking, augmented reality, and deep neural networks, supporting you to solve natural computer vision problems with practical code. You will be able to develop powerful computer vision applications in concise code with Open CV 4 and Python 3.
You will also learn the basic concepts of image processing, object categorization, and 2D as well as 3D tracking. Also, you can Train, utilize, and acknowledge machine learning models such as Support Vector Machines and neural networks. You will completely learn to execute real-world computer vision projects with the help of this book.
7. Programming Computer Vision With Python
The author of this book Jan Solem gives a simple and foundational understanding of computer vision’s fundamental theory and algorithms. You will learn the necessary techniques for object recognition, augmented reality, 3D reconstruction, stereo imaging, and multiple different computer vision applications as you go after comprehensible examples written in Python.
Also, In the book here, you will get to know all the methodologies utilized in the navigation of robots, image analysis in the medical domain. You will be provided with full code samples including explanations on way to reproduce and develop upon every example, accompanied by exercises to help you in the practical application of your knowledge.
8. Concise Computer Vision: An Introduction into Theory and Algorithms
This book can be called an introductory book on Computer vision. Classroom-focused exercises and review questionnaires are provided towards the end of every chapter. Features of this book include an introductory basic notation and mathematical concepts for expressing an image and the main concepts for mapping a particular image. This describes the topologic and geometric basics for analyzing the image domain and allocations of image values and discusses recognizing designs in the image.
Also introduced is optic flow for exhibit dense motion and various topics in sparse motion analysis; describes special approaches for image binarization and segmentation of motionless images or video frames. Another great feature of this book is it inspects the basic parts of a computer vision system and reviews various methods for computer vision-based 3D shape restructuring. Lastly, it Includes a conversation of stereo matches and even the phase-congruency model for image attributes; providing an introduction into learning and classification.
9. Computer Vision Metrics: Survey, Taxonomy, and Analysis (FREE BOOK)
This book gives the required background to build intuition about why interest point detectors, as well as feature descriptors, working patterns, the designing process, including observations related to tuning the processes for attaining strength and invariance targets for particular applications. The survey is wider than the actual depth, with over 540 references given to dig more deeply.
The taxonomy considers search methods, spectra components, descriptor depiction, shape, distance functions, robustness, accuracy, efficiency, and also invariance attributes, among many more. Instead of providing ‘how-to’ source code samples and shortcuts, this book gives supplemental discussion to multiple fine OpenCV community source codes and resources accessible for real-time practitioners.
10. Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
Written by Adrian Kaehler and Gary Bradski, creator of the OpenCV library, this book gives a thorough introduction to the topic targeted at developers, academics, roboticists, and enthusiasts. You’ll learn how to develop applications that allow computers to “see” and take up decisions depending on the data.
With more than 500 functions that extend to multiple areas in vision, OpenCV is utilized for business applications such as security, medical imaging, face and pattern recognition, robotics, and factory manufactures inspection. It also provides you a solid foundation in computer vision and OpenCV for developing simple as well as sophisticated vision applications. Hands-on exercises after every chapter will make you a practical expert in this field.
11. Computer Vision: Principles, Algorithms, Applications, Learning
One of the best CV books in the market. It is authored by E.R Davies, this is a completely revised fifth edition. It significantly and systematically shows the fundamental methodology of computer vision, wrapping the compulsory elements of the theory while focusing on algorithmic and practical design limitations.
This latest edition is in more of the concepts and real-time applications of computer vision, making it a very complete and up-to-date text worthy for undergraduate and graduate students, researchers, and also R&D engineers working in this vibrant field. This book also tells about the customized programming examples like code, methods, illustrations, tasks, and probable solutions (certainly involving MATLAB and C++).
Here Is An Extended List of Computer Vision Books