Learn how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open source framework written in Python. Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images. You’ll then learn how to apply these methods with SimpleCV, using sample Python code. All you need to get started is a Windows, Mac, or Linux system, and a willingness to put CV to work in a variety of ways. Programming experience is optional.

Capture images from several sources, including webcams, smartphones, and Kinect
Filter image input so your application processes only necessary information
Manipulate images by performing basic arithmetic on pixel values
Use feature detection techniques to focus on interesting parts of an image
Work with several features in a single image, using the NumPy and SciPy Python libraries
Learn about optical flow to identify objects that change between two image frames
Use SimpleCV’s command line and code editor to run examples and test techniques

About the Authors

Kurt is VP of Operations at Ingenuitas, and was a co-founder of Slashdot.

Anthony Oliver has worked in machine vision and robotics for 5 years with Big 3 automakers and other large manufacturers. He has written articles for Vision and Sensor Magazine, The online magazine H+, and given talks at Ignite Automotive and machine vision conferences.

Nathan Oostendorp has 15 of experience on running open source communities, being one of the founders of the website “Slashdot”, the site director for SourceForge and the creator of online communities PerlMonks and Everything2.

Katherine Scott is a graduate student at Columbia University specializing in the field of computer vision, has several peer reviewed papers published in the field of Augmented Reality, and several years of work related history in vision systems.

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