What is Harris Corner Detector
The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. Compared to its predecessor, Harris' corner detector takes the differential of the corner score into account with reference to direction directly, instead of using shifting patches for every 45 degree angles, and has been proved to be more accurate in distinguishing between edges and corners. Since then, it has been improved and adopted in many algorithms to preprocess images for subsequent applications.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Harris corner detector
Chapter 2: Corner detection
Chapter 3: Structure tensor
Chapter 4: Harris affine region detector
Chapter 5: Lucas-Kanade method
Chapter 6: Hessian matrix
Chapter 7: Geometric feature learning
Chapter 8: Tensor density
Chapter 9: Mehrotra predictor-corrector method
Chapter 10: Discrete Laplace operator
(II) Answering the public top questions about harris corner detector.
(III) Real world examples for the usage of harris corner detector in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Harris Corner Detector.
