OPTIMIZATION APPROACH TO EDGE-DETECTION

Most methods for finding the edges in a gray-scale image suffer from poor performance in a noisy environment. A novel optimization solution to this problem is explored. An optimization function, based on the output of a threshold-dependent edge detector, is constructed for modeling an a posteriori probability function for the edge image. The prior distribution for the edge image is formed as a Markov random-field model imbued with properties of edge images obtained in a noise-free environment. The edge image that maximizes the optimization function is generated with the simulated annealing algorithm. This edge-detection method, though computationally intensive, explores the use of higher-order Markov random fields that hold rich potential for varied applications. The performance of this approach at four signal-to-noise ratios is compared with that of standard gradient-based edge detection with real and simulated images.