Geometric Entities Voting Schemes in the Conformal Geometric Algebra Framework

Traditional methods for geometric entities resort to the Hough transform and tensor voting schemes for detect lines and circles. In this work, the authors extend these approaches using representations in terms of k-vectors of the Conformal Geometric Algebra. Of interest is the detection of lines and circles in images, and planes, circles, and spheres in the 3-D visual space; for that, we use the randomized Hough transform, and by means of k-blades we code such geometric entities. Motivated by tensor voting, we have generalized this approach for any kind of geometric entities or geometric flags formulating the perceptual saliency function involving k-vectors. The experiments using real images show the performance of the algorithms.