"If we want Machines to think, we need to teach them to see"- Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab. According to Fei Fei Li, Computer Vision is defined as a subset of mainstream Artificial Intelligence that deals with the science of making computers or machines visually enabled, i.e, they can analyze and understand an image. The idea behind computers to identify images like humans is amazing. Computer Vision has found its applications in various domains. Whether it is for detection, inspection, or navigation computer vision is used in a number of sectors, the prominent which I personally feel is the Security System. With so many applications, computer vision is also an interesting field that aspirants want to learn the deep concepts governing it. This article is supposed to make you familiar you some of the most useful books on Computer Vision which in turn will give you a thorough understanding of the field and will imbibe deeper understanding in you of the various concepts.
The book encompasses a variety of techniques required in analyzing and interpreting images by Computers. The various vision problems that are inquired during Computer vision are approached scientifically by the book. Detailed topics related to Linear Algebra, Numerical techniques, Bayesian estimation theory and exercises at the end of each chapter are provided in the book. It is a complete guide for any of the graduate in computer science or engineering.
The book will take you to a deeper understanding of the various computer vision methods. A wide range of mathematical methods in the treatment of computer vision methods will also be learned by you in this book. It will provide you a good understanding of the field and will make you proficient in building some useful applications.
As the name suggests the book will provide you a deep understanding of the various geometry concepts in dealing with the problems encountered in Computer vision techniques. You should be a little familiar with Linear Algebra and geometry before getting started with the book.
One of the most important Computer Vision problems such as camera calibration and Face recognition is discussed in the book giving out the understandable solutions to the readers. The Probabilistic models and the basics of probability that are required in computer vision are discussed in the book. This book will serve as a guide to most of the problems that you might inquire in computer vision.
This book is meant for Data Scientists and those who want to use Deep Learning in solving various problems of Computer Vision. Semantic Segmentation, Object Detection, Generative Models are various topics that are discussed in the book and will serve the best to you if you are already applying Deep Learning in various applications and have a good understanding of it.