ON THE DETECTION OF EDGES IN VECTOR IMAGES

A novel method for edge detection in vector images is proposed that do es not require any prior knowledge of the imaged scenes. In the deriva tion, it is assumed that the observed vector images are realizations o f spatially quasistationary processes, and that the vector observation s are generated by parametric probability distribution functions of kn own form whose parameters are in general unknown. The method detects a nd estimates the edge locations using a criterion derived by Bayesian theory. It chooses the number of edges and their locations according t o the maximum a posteriori probability (MAP) principle. We provide res ults that demonstrate its performance on synthesized and real images.