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A New Non-redundant Scale Invariant Interest Point Detector

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 avoiding redundant detections. Moreover, in this paper we present a differential geometry view to understand how interest points can be detected. We propose analyze the gaussian curvature to classify image regions as elliptical (blobs) or hyperbolic (corners or saddles). Our interest point detector has been compared with Harris-Laplace and Hessian-Laplace detectors on infrared (IR) images, outperforming their results in terms of the number and precision of interest points detected.