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Elastic reflection symmetry based shape descriptors

Kurtek, S., Shen, M. and Laga, H.ORCID: 0000-0002-4758-7510 (2014) Elastic reflection symmetry based shape descriptors. In: IEEE Winter Conference on Applications of Computer Vision (WACV) 2014, 24 - 26 March 2014, Sheraton Steamboat Springs, Colorado

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Reflection symmetry is an important feature of an object. Main goals in symmetry analysis include quantifying the amount of asymmetry in an object and finding the nearest symmetric object to a given asymmetric one. Samir et al. [19] achieved these goals using a shape distance between representations of curves termed square-root velocity functions. We extend their work by defining shape descriptors based on this representation. The descriptors are based on asymmetry measures computed for a set of reflections of a curve and are invariant to all shape preserving transformations (translation, scale, rotation and re-parameterization). We utilize these descriptors for retrieval of shapes in the Flavia leaf database and a subset of a handwritten digit dataset. We show that we outperform the commonly used angle function and other state of the art descriptors.

Item Type: Conference Paper
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