| View Larger Image | Computer Vision: A Modern Approach | Hardcoverby David A. Forsyth (Author), Jean Ponce (Author)
| List Price: | $129.00 | | Price: | $95.22 | | You Save: | $33.78 (26%) | | | Available: | Usually ships in 24 hours |
| | Binding: | Hardcover | | Publisher: | Prentice Hall | | Edition: | US edth Edition | | Page Count: | 693 Pages | | Publication Date: | August 24, 2002 | | Sales Rank: | 566,076th |
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EDITORIAL REVIEWS |
Product Description
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise. |
CUSTOMER REVIEWS (Average Customer Rating: 2.5 based on 20 reviews)
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complete mess by Yen Cheng Chen  1 Stars
September 15, 2008
this book is incredibly hard to understand and I would not recommend it to anyone.
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Must read by M. Tepper  5 Stars
April 20, 2008
If you're into Computer Vision this is a must read. The concepts are well explained, and the topics covers all the basic things to know about CV. The edition is very good.
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Terrible book, Hard to understand! by Will Yan (IL ,USA) 1 Stars
January 25, 2008
It is hard for me, as a Ph.D. graduate student to understand this book. The content is not well organized and many terms are not defined before using.
Not recommend for any person.
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Broad, but a mess. by D. Mauer  2 Stars
October 23, 2006
This is, from what I can tell, just about the most complete up-to-date text in the field of computer vision as of late 2006.
But it's a mess.
I'm a PhD student, and have worked my way through more than my fair share of high-level computer science textbooks. This one makes me really appreciate many of them. It reads like a first draft -- overly wordy at times, skipping over important issues, poorly organized... Some concepts that ought to be really simple are made very painful due to what seems to be laziness on the part of the editor. It's like the only people that critiqued this book prior to publication already knew all there is to know about computer vision.
A particularly nasty aspect of this book is that the authors have a horrible habit of including some term in some complex mathematical formula, with no reference whatsoever to that term in the surrounding text! In an explanation of how to use Expectation Maximization in line-fitting, they include a standard-deviation term, with no mention of how you're supposed to choose a value for it other than "...for sigma as before". The only "before" in which the SD (sigma) is mentioned in a similar context that I can find is IN THE PREVIOUS CHAPTER!!!
Anyway, if you want to try to teach yourself vision, don't bother. If you need the book for a class, I'm sorry it's so expensive. Either way, don't expect much.
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Computer vision by Antonio Giovanni Lezzi (italy) 5 Stars
July 29, 2006
I think this book is the most complete computer vision arguments. In fact it start to speaks from radiometry to steriovision passing by filter uses!!
Good very good
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SIMILAR PRODUCTS |
| Multiple View Geometry in Computer Vision
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A basic problem in computer vision is to understand the structure of a real world scene. This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First...
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| Pattern Recognition and Machine Learning (Information Science and Statistics)
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The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of...
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| Introductory Techniques for 3-D Computer Vision
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Senior/Graduate level courses on computer vision, robot vision and image processing in electrical and computer engineering, mathematics, and computer science departments, and an essential reference for researchers and scientists in the field of computer vision. An applied introduction to modern computer vision, focusing on a set of computational techniques for 3-D imaging. Covers a wide range of fundamental problems encountered within computer vision and provides detailed algorithmic and...
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| Learning OpenCV: Computer Vision with the OpenCV Library
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"This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised." -William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and...
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| Computer Vision
by Linda G. Shapiro (Author), George C. Stockman (Author)
(Pearson Education) A textbook and reference for students and practitioners, presenting the necessary theory for work in fields where significant information must be extracted from images. Topics covered include databases and virtual and augmented reality, and the text includes more than 250 exercises and programming projects. DLC: Computer vision.
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