We present a new method for detecting interest points using histogram information. Unlike existing interest point detectors, which measure pixel-wise differences in image intensity, our detectors incorporate histogram-based representations, and thus can find image regions that present a distinct distribution in the neighborhood. The proposed detectors are able to capture large-scale structures and distinctive textured patterns, and exhibit strong invariance to rotation, illumination variation, and blur. The experimental results show that the proposed histogram-based interest point detectors perform particularly well for the tasks of matching textured scenes under blur and illumination changes, in terms of repeatability and distinctiveness. An extension of our method to space-time interest point detection for action classification is also presented
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