In computer vision, the concept of interest points—also called keypoints or feature points—has been largely used to solve many problems in object recognition, image registration, visual tracking, 3D reconstruction, and more. This concept relies on the idea that instead of looking at the image as a whole, it could be advantageous to select some special points in the image and perform a local analysis on them. This approach works well as long as a sufficient number of such points are detected in the images of interest and these points are distinguishing and stable features that can be accurately localized.

Because they are used for analyzing image content, feature points should ideally be detected at the same scene or object location no matter from which viewpoint, scale, or orientation the image was taken. View invariance is a very desirable property in image analysis and has been the object of numerous studies. As we will see, different detectors have different invariance properties...