An experimental comparison of a hierarchical range image segmentation algorithm

This paper describe a new algorithm to segment range images into continuous regions represented by Bezier polynomials. The main problem in many segmentation algorithms is that it is hard to accurately detect at the same time large continuous regions and their boundary location. In this paper, a Bayesian framework is used to determine through a region growing process large continuous regions. Following this process, an exact description of the boundary of each region is computed from the mutual intersection of the extracted parametric polynomials followed by a closure and approximation of this new boundary using a gradient vector flow algorithm. This algorithm is capable of segmenting not only polyhedral objects but also sculptured surfaces by creating a network of closed trimmed Bezier surfaces that are compatible with most CAD systems. Experimental results show that significant improvement of region boundary localization and closure can be achieved. In this paper, a systematic comparison of our algorithm to the most well known algorithms in the literature is presented to highlight its performance.