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Introductory Techniques for 3-D Computer Vision
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Provides 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 theoretical solutions for each. Each chapter concentrates on a specific problem and solves it by building on previous results. 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. MARKETS: For researchers and scientists in the field of computer vision, electrical and computer engineers. Also suitable for students in advanced studies within these areas.
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Product details
Paperback: 343 pages
Publisher: Prentice Hall (March 16, 1998)
Language: English
ISBN-10: 0132611082
ISBN-13: 978-0132611084
Product Dimensions:
7 x 0.8 x 9.1 inches
Shipping Weight: 1.3 pounds (View shipping rates and policies)
Average Customer Review:
3.7 out of 5 stars
12 customer reviews
Amazon Best Sellers Rank:
#1,255,527 in Books (See Top 100 in Books)
This is a very precise and well organized books. Every chapter is well justified and moreover each chapter contains relevant algorithms and psuedocode to address the problems discussed. This makes the book particularly practical and useful. Topics discussed follow each other naturally and the authors have done a great job of covering the topics conisely without going overboard.
I was a bit disappointed by this book. It is written more like a reference book than something you can read through to learn the material. It is full of various equations with little in the way of "plain english" explanations. If you are VERY comfortable with vector math and looking at lots of equations, then this book may be a good reference. If you are looking for understanding basic concepts, then you will need to look elsewhere.
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.
I didn't know or hear about this book till one fine morning when I went to our IIT library looking for some good book on computer vision to supplement the knowledge imparted by Horn's book. I found this book and it contained most of the concepts covered thus far in my lectures. Still I was not convinced about the credibility of this book. Somehow, I started grazing through this book in leisure hours.To my surprise, I found that it was simply an amazing book written so skillfully on computer vision from the basics. The Math fundae in the appendix were the ones I read first. They were very concise and helped me to grasp the concepts quickly. The chapters were also based on recent literature and very much coherent and self-explanatory.This book has the potential to become a master-piece in computer vision. One unique feature of this book is the clear explanation of Math concepts in each chapter. For it to become more user-friendly, some real application oriented problems should be added.But, on the whole, this book is an excellent book to be read along with Horn's book to fully understand the basics of computer vision. I strongly recommend this book to any novice to computer vision with little understanding of image processing concepts.
I originally purchased this book for a graduate-level computer vision course. I found the book more obfuscating than helpful. For example, Trucco and Verri's discussion of PCA/SVD, a topic I know quite a bit about, left me confused and frustrated. Unfortunately, their treatment of other topics is not any better.I have since used the book as a reference on a couple occasions. My opinion of the book has been raised only slightly since my first read, taking my rating all the way up to two stars ;). I suggest that you DO NOT waste your money on this book.
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.
Every newcomer to the field of computer vision should own this book. With a well organized blend of theory and algorithms, this text clearly and (as important) succinctly introduces the key elements of computer vision. This text has the best introduction to epipolar geometry that I've seen. For a reference, it's very thin. However, for an introductory text, it is hard for me to imagine a much better one. I am not aware of any book that is even comparable.
It is an interesting book that can be read with easiness recommended for all the courses of artificial vision, its brings important algorithms, annotations and interesting internet addresses where one can know more about this topic. The division of the book is interesting sandal in an easy way the topic of the vision stereoscopy, connecting the different topics and allowing to the reader, with a simple but technical leguaje, to select those that more they make comfortable to its necessities.
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