Perceptual organization approach based on Dempster-Shafer theory

In this paper, we propose an application of the perceptual organization based on the Dempster-Shafer theory. This method is divided into two parts which respectively rectifies the segmentation mistakes by restoring the coherence of the segments and detects objects in the scene by forming groups of primitives. We show how we apply the Dempster-Shafer theory, usually used in data fusion, in order to obtain an optimal adequation between the perceptual organization problem and this tool. We show that without any prior knowledge and any threshold, our bottom-up algorithm detects efficiently the different objects even in cluttered environment. Moreover, we demonstrate its robustness and flexibility on indoor and outdoor scenes without any modification of parameters. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.