Computer vision is a part of artificial intelligence (AI) that allows computer networks and systems to extract beneficial data from virtual photos, videos, and different visible inputs, additionally on behavior moves or make tips primarily based totally on that data. Computer vision is practiced in different industries, including energy, utilities, manufacturing, and automotive, and the industry is still growing. These are the top 10 computer vision books that will help you learn about computer vision (CV). Interested readers should give those books a look to expand their knowledge related to computer vision.
1. Computer Vision: Algorithms and Applications (Texts in Computer Science) 2011th Edition
By Richard Szeliski
Algorithms and Applications look into the various methods for analyzing and understanding photographs. It also highlights complex real-world scenarios in which vision is successfully used, both for specialized applications like medical imaging and for enjoyable, consumer-level tasks like image editing and stitching, which students can apply to their images and movies.
This textbook is best suited for upper-level undergraduate or graduate-level computer science or engineering courses. It focuses on basic strategies that work in real-world situations and pushes students to stretch their creative bounds.
Its layout and presentation also make it ideal as a one-of-a-kind reference to computer visualization’s fundamental techniques and current research literature.
2. Computer Vision: Models, Learning, and Inference 1st Edition
By Simon J. D. Prince
This modern book on computer vision focuses on learning and inference in probabilistic models. It explains how to leverage schooling data to investigate the correlations between discovered image data and attributes of the sector that need to be estimated, such as 3-D shape or item class, and how to use those associations to derive new conclusions about the sector from new picture data.
The unique methodological presentation is aimed mainly at advanced undergraduate and graduate students, but it can also be useful for computer vision practitioners.
Graph cutting, gadget learning, and multiple-view geometry are among the current techniques covered in this book.
More than 70 algorithms have been specified in great detail to be applied. Different illustrations are used in the book to support the concept of computer vision.
3. Computer Vision: A Modern Approach (2nd Edition)
By Forsyth Ponce
This book offers a fresh perspective on computer vision. This book is important in offering a strategic overview of computer vision, as it provides a comprehensive examination of the entire computer vision enterprise as well as sufficient depth for readers to be able to design practical applications.
The book provides the most coherent possible synthesis of current ideas, stressing strategies that have been successful in constructing applications, through substantial use of probabilistic methods—topics have been picked for their importance, both practically and theoretically.
This book will be useful to readers interested in computer graphics, robotics, image processing, and imaging in general.
4. Practical Deep Learning for Cloud, Mobile, and Edge
By Anirudh Koul
Whether you’re a software engineer or computer scientist looking to break into the deep mastering field, this book provides you an opportunity with the simple goal of inventing the next viral AI app.
Using a hands-on methodology, this step-by-step guide teaches you how to build practical mastering applications for the cloud, mobile, browsers, and part devices. This book helps you build something innovative, useful, scalable, or simply undeniably cool.
This book will guide you through the steps of developing a product that people can use in the real world. Keras, TensorFlow, Core ML, and TensorFlow Lite are used to train, adjust, and deploy computer vision models. This book also uses transfer learning to train models in minutes for a variety of devices, including the Raspberry Pi, Jetson Nano, and Google Coral.
5. Multiple View Geometry in Computer Vision 2nd Edition
By Richard Hartley
This book provides an understanding of the structure of a real-world scene from several photographs in a fundamental way in the computer vision field. Projectile geometry and photogrammetry techniques are used to solve this challenge.
The authors discuss geometric principles and their algebraic representations in terms of camera projection matrices, the basic matrix, and the trifocal tensor in this chapter. The new version includes an expanded introduction that covers the book’s major themes (which have been updated with new examples and appendices) as well as significant new findings since the first edition.
Readers who are familiar with linear algebra and basic numerical methods will be able to understand the projective geometry and estimation algorithms presented and will be able to implement the algorithms directly from the book.
6. Learning OpenCV 4 Computer Vision with Python 3
By Joseph Howse, Joe Minichino
Computer vision is a rapidly evolving field that includes a wide range of applications and approaches. This book will benefit both beginners and experts in the field of computer vision. Building applications with OpenCV and Python 3 allows you to put theory into reality.
This book will lead you through image processing, video analysis, depth estimation, and segmentation, as well as how to construct GUI apps in practice. The reader will also learn about object classification and machine learning techniques in order to develop and apply object detectors and classifiers, as well as track things in images captured by a film or video camera.
After finishing this book, you may have the abilities in order to perform and execute computer vision projects.
7. Programming Computer Vision with Python
By Jan Erik Solem
This hands-on introductory book is the best place to start if you want a basic understanding of computer’s imaginative and intuitive underlying principles and algorithms. As you follow clear Python examples, you’ll examine methods for item recognition, 3-D reconstruction, stereo imaging, augmented reality, and various computer imaginative and prescient packages. Programming Computer Vision with Python is a book that teaches computer vision in simple terms so you don’t get frustrated in theory. You’ll get entire code samples with instructions on how to replicate and build on each example, as well as exercises to help you put what you’ve learned into practice.
This book is ideal for students, researchers, and enthusiasts who have a basic understanding of programming and mathematics.
8. Practical Computer Vision with SimpleCV
By Kurt Demaagd, Anthony Oliver, Nathan Oostendorp, Katherine Scott
This book teaches how to use SimpleCV, a Python-based open-source framework, to quickly and easily create your computer vision (CV) applications. This book also works as a manual that introduces you to primary computer vision methodologies for collecting, analyzing, and observing streaming virtual images by using examples from real-world systems. Then, using pattern Python code, the reader will learn how to examine those approaches with SimpleCV.
9. Mastering OpenCV 4 with Python
By Alberto Fernandez Villan
In this book, the reader will begin by installing OpenCV and getting into the fundamental concepts of computer vision. The reader will then go on to more advanced principles and uncover OpenCV’s full potential.
The book will also teach you sophisticated application development using Python and OpenCV, allowing you to create apps such as facial recognition, target tracking, and augmented reality.
In the last chapters, the reader will look at how to use artificial intelligence and deep learning techniques with the popular Python libraries TensorFlow and Keras.
By the end of this book, you’ll be able to create powerful computer vision applications to fulfill the needs of your clients.
10. Introductory Techniques for 3-D Computer Vision
By Emanuele Trucco
This book is all about modern techniques in computer vision, and making a solid base for computer engineers to focus on the computational art of 3-D imaging. This book’s range is very wide covering fundamental computer vision issues and providing extensive algorithmic and theoretical answers. Every chapter focuses on a single problem and addresses it by building on prior successes.
In the book, two parallel tracks are developed, demonstrating how fundamental problems are solved utilizing both intensity and range images, the most often utilized forms of images today.
For those students who are in advanced computer engineering or its related field, this book will help them grow in the technical field as well as in knowledge.
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