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  • Book Overview & Buying Mastering OpenCV with Practical Computer Vision Projects
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Mastering OpenCV with Practical Computer Vision Projects

Mastering OpenCV with Practical Computer Vision Projects

4.2 (17)
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Mastering OpenCV with Practical Computer Vision Projects

Mastering OpenCV with Practical Computer Vision Projects

4.2 (17)

Overview of this book

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials.Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV's new C++ interface before migrating from the C API to the C++ API.Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you're most interested in.Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.
Table of Contents (15 chapters)
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Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1
Index

Using feature descriptors to find an arbitrary image on video


Image recognition is a computer vision technique that searches the input image for a particular bitmap pattern. Our image recognition algorithm should be able to detect the pattern even if it is scaled, rotated, or has different brightness than of the original image.

How do we compare the pattern image against other images? As the pattern can be affected by perspective transformation, it's obvious that we can't directly compare pixels of the pattern and test image. The feature points and feature descriptors are helpful in this case. There is no universal or exact definition of what the feature is. The exact definition often depends on the problem or the type of application. Usually a feature is defined as an "interesting" part of an image, and features are used as a starting point for many computer vision algorithms. In this chapter we will use a feature point term, which is a part of the image defined by a center point, radius...

Visually different images
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83
Tech Concepts
36
Programming languages
73
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