The SURF and SIFT keypoint detection algorithms, discussed in Chapter 8, Detecting Interest Points, define a location, an orientation, and a scale for each of the detected features. The scale factor information is useful to define the size of a window of analysis around each feature point. Thus, the defined neighborhood would include the same visual information no matter what the scale of the object to which the feature belongs has been pictured. This recipe will show you how to describe an interest point's neighborhood using feature descriptors. In image analysis, the visual information included in this neighborhood can be used to characterize each feature point in order to make each point distinguishable from the others. Feature descriptors are usually N-dimensional vectors that describe a feature point in a way that is invariant to change in lighting and to small perspective deformations. Generally, descriptors can be compared using simple distance...
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You're reading from OpenCV Computer Vision Application Programming Cookbook Second Edition
Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content based video analysis, visual surveillance, driver-assistance, object detection, and tracking.
Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development published by McGraw Hill in 2001. He co-founded Visual Cortek in 2006, an Ottawa-based video analytics start-up that was later acquired by iwatchlife.com in 2009. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of start-up companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.
Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987) and MSc and PhD degrees from INRS-Telecommunications, Montreal (1996). You can visit the author's website at laganiere.name.
Read more about Robert Laganiere
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Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content based video analysis, visual surveillance, driver-assistance, object detection, and tracking.
Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development published by McGraw Hill in 2001. He co-founded Visual Cortek in 2006, an Ottawa-based video analytics start-up that was later acquired by iwatchlife.com in 2009. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of start-up companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.
Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987) and MSc and PhD degrees from INRS-Telecommunications, Montreal (1996). You can visit the author's website at laganiere.name.
Read more about Robert Laganiere