Introductory Techniques for 3-D Computer Vision [ペーパーバック]

If you already understand image processing and the basics of computer vision, this book is a very good at concisely presenting more advanced algorithms to the reader. Also, because this book is so well organized, you can read it from beginning to end. Rest assured if you are looking at an algorithm on page 84, you will not need to skip ahead to later sections in the book to understand it. From the beginning, algorithms are named and presented in numbered steps for clarity of presentation. The book starts out with introductory material such as basic optics and the geometry of camera models. It continues with image denoising, as well as two full chapters devoted to image features and their detection. Finally, the more basic material concludes with a chapter on the mathematics of camera calibration. One aspect of vision that is often neglected in other computer vision books that is treated well here is that of motion. For those working in video processing, this might make this book a good selection. Also, the book gives one of the best discussions of eigenspaces that I have seen in print in chapter ten of the book, where the subject is recognition of 3D objects. I was able to code up the eigenfaces face recognition algorithm based almost entirely on the information found in chapter ten of this book. If you need an introduction to computer vision before tackling the more advanced material in this text, try Shapiro's book "Computer Vision" ISBN 0130307963. A good knowledge of linear algebra is necessary prior to understanding the algorithms in this book such as is found in Schaum's outline of Matrix Operations. Given the specific subject matter of this book, it would probably be an excellent choice for an engineer or scientist that is interested in computer vision as it relates to robotics.

This is the best textbook for computer vision I've ever met. Compared with Forsyth and Ponce's heavy book titled with "computer vision: a modern approach", this book is very concise, precise, and suitable to be used in classes. Definitions are carefully given. Pseudocode is extremely helpful for practical use. Also some sections about mathematics are appreciated to understand the underlying algorithms. If you want to learn more or do research, then read Forsyth's book after finishing reading this book. Several my collegues think this book is the first choice for new comers in this field.

It's a pretty good reference for the one who want to start in computer vision ,espectially in stereo graphic field. With its comprehensive explanation and well oganization,it should be the first book in your computer vision bookshelf.