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Learning OpenCV: Computer Vision with the OpenCV Library Paperback – Sep 24 2008

4.3 out of 5 stars 4 customer reviews

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Product Details

  • Paperback: 580 pages
  • Publisher: O'Reilly Media; 1 edition (Oct. 4 2008)
  • Language: English
  • ISBN-10: 0596516134
  • ISBN-13: 978-0596516130
  • Product Dimensions: 17.8 x 3 x 23.3 cm
  • Shipping Weight: 771 g
  • Average Customer Review: 4.3 out of 5 stars 4 customer reviews
  • Amazon Bestsellers Rank: #283,076 in Books (See Top 100 in Books)
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Product Description

Book Description

Software That Sees

About the Author

Dr. Gary Rost Bradski is a consulting professor in the CS department at Stanford University AI Lab where he mentors robotics, machine learning and computer vision research. He is also Senior Scientist at Willow Garage, a recently founded robotics research institute/incubator. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University. He has 20 years of industrial experience applying machine learning and computer vision spanning option trading operations at First Union National Bank, to computer vision at Intel Research to machine learning in Intel Manufacturing and several startup companies in between. Gary started the Open Source Computer Vision Library (OpenCV​opencvlibrary/ ), the statistical Machine Learning Library (MLL comes with OpenCV), and the Probabilistic Network Library (PNL). OpenCV is used around the world in research, government and commercially. The vision libraries helped develop a notable part of the commercial Intel performance primitives library (IPP Gary also organized the vision team for Stanley, the Stanford robot that won the DARPA Grand Challenge autonomous race across the desert for a $2M team prize and helped found the Stanford AI Robotics project at Stanford working with Professor Andrew Ng. Gary has over 50 publications and 13 issued patents with 18 pending. He lives in Palo Alto with his wife and 3 daughters and bikes road or mountains as much as he can.

Dr. Adrian Kaehler is a senior scientist at Applied Minds Corporation. His current research includes topics in machine learning, statistical modeling, computer vision and robotics. Adrian received his Ph.D. in Theoretical Physics from Columbia university in 1998. Adrian has since held positions at Intel Corporation and the Stanford University AI Lab, and was a member of the winning Stanley race team in the DARPA Grand Challenge. He has a variety of published papers and patents in physics, electrical engineering, computer science, and robotics.

Customer Reviews

4.3 out of 5 stars
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Top Customer Reviews

Format: Paperback
I purschased this book to help me in using OpenCV for a Block-Based Motion Compensation Application. The first 3 chapters give you all the info you need to learn the core of OpenCV. So I stopped reading at that point since I did not need to use any vision related stuff.

I recommend this book, because there is no other reliable source of information on OpenCV. The OpenCV Wiki is weak and is filled with outdated stuff. The book is not perfect but it help you get the job done.

I also recommend OpenCV, the API is very consistent but not perfect. Once you get used to it you appreciate some of its subtilities.
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I bought this book in an attempt to learn openCV. However, almost every example in this book doesn't work with the current version of openCV. The book is good to have a feeling about the workings of openCV, but be prepared to debug the examples in it as it will not work as is.
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Format: Paperback Verified Purchase
Whether you want to use the OpenCV library in your projects, or just want a good introduction to computer vision, this book is your best bet. It's simple, well written, and fairly comprehensive. I would argue that you would be better off purchasing this book over any theoretical computer vision text if your goal is an introduction to the field.

For example: the explanation of the Kalman filter in this text is far more intelligible than any I've seen elsewhere. It's pretty remarkable - you will have a better understanding of the Kalman filter if you read this text (which documents a computer vision library) than if you read a paper on it! The reason for this is that by necessity there is a strong practical bias in this book, so that the authors can't just throw a wishy-washy formula on the page and hand wave their way around the details. That nuts and bolts level explanation is absolutely essential if you want to get anywhere - whether you're goal is to appy computer vision, or to develop novel algorithms.

Without reservation, I will say that this is the best computer vision book in my library. So in summary, you have to buy this book. You have to buy now. Did you buy it yet?
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Format: Paperback
Shipped form Canada to the US. Beat any other price in the US by far.
And the content of the book was better than I hoped.
Looking forward to using OpenCV in my projects.
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Most Helpful Customer Reviews on (beta) HASH(0x9c87f9f0) out of 5 stars 49 reviews
54 of 55 people found the following review helpful
HASH(0x9c887540) out of 5 stars A great guide to OpenCV with plenty of context Oct. 30 2008
By calvinnme - Published on
Format: Paperback Verified Purchase
This book is excellent at exposing the reader to the various methods available in OpenCV and showing via code examples how to use each one. The author also gives you the website where you can look at the actual source code of each method shown. This is helpful since, for example, if you want to know exactly how the code is going about calculating the Fundamental Matrix, it is difficult to determine this by reading the book alone.

This book would be most useful to someone who already has a fundamental understanding of computer vision and image processing and wants to see how OpenCV will make their programming tasks easier. It does this by coding up well known algorithms into reliable pieces of code that you can use to accomplish more complex tasks. Do not come to this book if you are seeking to learn computer vision. You will only be confused as the author does not offer enough detail to teach you the mathematical foundations. However, I don't think that was his intention at all. Instead it is part user manual, part basic computer vision tutorial and overview, and part idea book. Each chapter is supplemented with excellent and interesting programming exercises that test your knowledge of what has been presented in a practical setting.

For a good basic understanding of computer vision try Computer Vision. To understand the algorithmic underpinnings of 3D computer vision try Introductory Techniques for 3-D Computer Vision. However, before you read either of these you must read Digital Image Processing (3rd Edition), since image processing concepts are fundamental to understanding computer vision tasks. In fact, the two disciplines overlap in many spots. The sad truth of the matter is that no one book will teach you what you need to know to be an effective image scientist. However, this book on OpenCV is essential reading on applying the theory via programming in an effective manner. Highly recommended.
68 of 73 people found the following review helpful
HASH(0x9c887894) out of 5 stars Lacking the C++ API Nov. 18 2010
By Peter Harrington - Published on
Format: Paperback Verified Purchase
I really love OpenCV. I bought this book and read about 50% of it before starting a project. Initially I found some code on the internet that looked like OpenCV code but was lacking pointers and casts. I learned that this clean code is actually C++ code with heavy use of templates in OpenCV 2.0. Sadly the book is based on OpenCV 1.0, so very little of the code in the book is useable.
8 of 8 people found the following review helpful
HASH(0x9c887858) out of 5 stars Aimed for Computer Vision Researchers Dec 17 2010
By howlowck - Published on
Format: Paperback
This is a great book on the library. Although it's rather old and lacks the new C++ API (which, to me, is very similar to the MATLAB image processing toolkit), it provides a rather detailed explanation on the inner workings of the library as well as the math and research papers that backs it up.

If you are just a CV hobbyist, and just want to create some computer vision app with a webcam and don't care about the math, there's tons of resource online on how to use the library effectively.

I give the book four stars instead of five because I wish it would give more programming examples that it does. And sometimes the language is a little hard to understand, I'd usually have to read it several times to grasp the concepts in the book.
10 of 11 people found the following review helpful
HASH(0x9c887d50) out of 5 stars An absolute must have!!! Oct. 20 2008
By Jean-Yves Bouguet - Published on
Format: Paperback
At last a practical, pragmatic, accessible book on computer vision (and more!) providing step by step guidance on fundamental computational vision topics, with algorithmic explanation (just what is needed!), and concrete example code snippets. This book is now opening the door to the fabulous world of computational vision to anyone. It gives immediate access to a vast collection of image processing, and machine learning functions, all open source!
The book also includes many references and pointers to other material (such as technical papers), allowing the reader to learn more about any topic covered.
This is a great reference book, that won't just sit on your self.
10 of 11 people found the following review helpful
HASH(0x9ca0b060) out of 5 stars Great Toolkit, Excruciating Read July 31 2010
By Renner - Published on
Format: Paperback
The OpenCV library is great and it is nice that somebody put together a book to document it, because the documentation on the opencv wiki is as good as it could be and not well organized. The shortcoming of this book is the extreme verbosity. It is almost excruciating to read because you cannot just skip to the important points because many important points are buried enormous paragraphs that are mostly fluff. The two stars that I give it are entirely for my appreciation of the subject matter.