Introductory Techniques for 3-D Computer Vision

Features

• Cover Type: Paperback with 343 pages
• Published by: Prentice Hall March 16, 1998
• Written in: English
• ISBN 10 Number: 0132611082
• ISBN 13 Number: 978-0132611084
• Book Dimensions: 9.1 x 7 x 0.8 inches
• Weighs: 1.3 pounds

Back Cover Copy


An applied introduction to modern computer vision, focusing on a set of computational techniques for 3-D imaging, this book covers a wide range of fundamental problems encountered within computer vision and provides detailed algorithmic and theoretical solutions for each. Each chapter concentrates on a specific problem and solves it by building on previous results.

Features:

• Provides a guide to well-tested theory and algorithms including solutions of problems encountered in modern computer vision.
• Contains many practical hints highlighted in the book.
• Develops two parallel tracks in the presentation, showing how fundamental problems are solved using both intensity and range images, the most popular types of images used today.
• Each chapter contains notes on the literature, review questions, numerical exercises, and projects.
• Provides an Internet list for accessing links to test images, demos, archives and additional learning material.

Reader Reviews
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.