SYMMETRY DETECTION BY RANDOM SAMPLING AND VOTING PROCESS FOR MOTION ANALYSIS

Abstract

Symmetry of an object on a plane and in a space is an important geometric feature for biology, chemistry, and the understanding of human perception of figures. We propose a randomized method for the detection of symmetry in planar polygons and polyhedrons without assuming the predetermination of the centroids of the objects. Using a voting process, which is the main concept of the Hough transform in image processing, we transform the geometric computation for symmetry detection which is usually based on graph theory and combinatorial optimization, to the peak detection problem in a voting space in the context of the Hough transform. Our algorithm detects the centroid after detecting symmetry of an object for both planar and spatial objects.