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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 demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:

  • A thorough introduction to OpenCV

  • Getting input from cameras

  • Transforming images

  • Segmenting images and shape matching

  • Pattern recognition, including face detection

  • Tracking and motion in 2 and 3 dimensions

  • 3D reconstruction from stereo vision

  • Machine learning algorithms

Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

Subscriber Reviews

Average Rating: 4.571428571428571 out of 5 rating Based on 7 Ratings

"Learning OpenCV, 1st Edition " - by Anonymous on 30-JUN-2010
Reviewer Rating: 1 star rating2 star rating3 star rating4 star rating5 star rating
I wish the book explained more clearly what's happening under the covers. In parts, it's more of a cookbook. For instance in the histogram section there are many metrics for comparing 2 histograms. What they're actually measuring isn't explained.
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