A scale invariant interest point detector for discriminative blob detection

Abstract

In this paper we present a novel scale invariant interest point detector of blobs which incorporates the idea of blob movement along the scales. This trajectory of the blobs through the scale space is shown to be valuable information in order to estimate the most stable locations and scales of the interest points. Our detector evaluates interest points in terms of their self trajectory along the scales and its evolution obtaining non-redundant and discriminant features. Moreover, in this paper we present a differential geometry view to understand how interest points can be detected. We propose to analyze the gaussian curvature to classify image regions as blobs, edges or corners. Our interest point detector has been compared with some of the most important scale invariant detectors on infrared (IR) images, outperforming their results in terms of: number of interest points detected and discrimination of the interest points. © 2009 Springer Berlin Heidelberg.