Region description using scalable circumferential filters

In this paper, we present a technique to describe local image features using scalable circumferential filters. Region description is the basic technique for many computer vision applications such as visual tracking, matching, and object recognition. In the first part of this paper, we present an elliptical cylinder projection method to geometrically normalize the ellipse regions to circular. In the second part of the paper, a set of scalable circumferential filters are proposed to extraction the distinctive feature of each region. Unlike traditional image filters, the shape of the proposed circumferential filters is fan and is scalable with its distance to the region's center. Experiments on typical images exhibit the robustness of the proposed method. Extensively quantitative evaluation and comparison demonstrate that the proposed method outperforms state-of-the-art method.

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