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The paper provides a review of contour polar shape descriptors used in recognition of objects based on their silhouettes. The process of recognition in the template matching approach has to be based on so called descriptors, assigned to object features, e.g. shape, texture, color, luminance, context of the information and movement. Amongst them very special attention is paid to the shape, because in many applications it is the most relevant and the less changeable feature that can be used. The shape in the digital image processing has usually a form of binary object. One of the representations uses the boundary, contour of a silhouette. The most important advantage of such approach is a small number of pixels to consider. Amongst several dozen shape descriptors special properties can be found in the polar ones, which use the transformation from the Cartesian to the polar coordinates. The most important is invariance to translation of the object points. The rotation becomes a circular shift what can be easily solved in further processing. Owing to the normalization the descriptors can be also invariant to scaling. Some of the methods are also robust to some level of noise and occlusion.

Polar contour shape descriptors in the template matching approach to object recognition