A scale-space approach for multiscale shape analysis
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Currently, given the widespread of computers through society, the task of recognizing visual patterns is being more and more automated, in particular to treat the large and growing amount of digital images available. Two well-referenced shape descriptors are BAS (Beam Angle Statistics) and MFD (Multiscale Fractal Dimension). Results obtained by these shape descriptors on public image databases have shown high accuracy levels, better than many other traditional shape descriptors proposed in the literature. As scale is a key parameter in Computer Vision and approaches based on this concept can be quite successful, in this paper we explore the possibilities of a scale-space representation of BAS and MFD and propose two new shape descriptors SBAS (Scale-Space BAS) and SMFD (Scale-Space MFD). Both new scale-space based descriptors were evaluated on two public shape databases and their performances were compared with main shape descriptors found in the literature, showing better accuracy results in most of the comparisons.