When searching for interesting feature points in images, corners come out as an interesting solution. They are indeed local features that can be easily localized in an image, and in addition, they should abound in scenes of man-made objects (where they are produced by walls, doors, windows, tables, and so on). Corners are also interesting because they are two-dimensional features that can be accurately localized (even at sub-pixel accuracy), as they are at the junction of two edges. This is in contrast to points located on a uniform area or on the contour of an object and points that would be difficult to repeatedly localize precisely on other images of the same object. The Harris feature detector is a classical approach to detecting corners in an image. We will explore this operator in this recipe.

OpenCV Computer Vision Application Programming Cookbook
By :

OpenCV Computer Vision Application Programming Cookbook
By:
Overview of this book
Table of Contents (18 chapters)
OpenCV Computer Vision Application Programming Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Playing with Images
Manipulating Pixels
Processing Color Images with Classes
Counting the Pixels with Histograms
Transforming Images with Morphological Operations
Filtering the Images
Extracting Lines, Contours, and Components
Detecting Interest Points
Describing and Matching Interest Points
Estimating Projective Relations in Images
Processing Video Sequences
Index
Customer Reviews