Abstract
In this work the authors introduce a novel geometric voting scheme that extends previous algorithms, like Hough transform and tensor voting, in order to tackle perceptual organization problems. Our approach is grounded in three methodologies: representation of information using Conformal Geometric Algebra, a local voting process, which introduce global perceptual considerations at low level, and a global voting process, which clusters salient geometric entities that are supported in the whole image. Geometric algebra provides a suitable mathematical framework, which allows our algorithm to infer high-level geometric representations of percepts in an image. Experiments show the capability of our algorithm for representing objects in images in terms of circles and lines, even though it contains a noisy input, incomplete data, illusory or non-linear contours.