A scale invariant keypoint detector based on visual and geometrical cues

Abstract

One of the first steps in a myriad of Visual Recognition and Computer Vision algorithms is the detection of keypoints. Despite the large number of works proposing image keypoint detectors, only a few methodologies are able to efficiently use both visual and geometrical information. In this paper we introduce KVD (Keypoints from Visual and Depth Data), a novel keypoint detector which is scale invariant and combines intensity and geometrical data. We present results from several experiments that show high repeatability scores of our methodology for rotations, translations and scale changes and also presents robustness in the absence of either visual or geometric information.