Low-Level Vision Edge Detector By Using Bayesian Decision And Maximum A Posteriori Probability Estimation Theory

This paper presents an automated approch for a low-level vision edge detector. The approach we have taken is to formulate the problem in terms of Bayesian inferencing. This provides meaningful performance functionals. The focus of this work is on the use of Markov Random Fields for specifying the a priori probability for an object or a scene. Local models for regions and edges in the image are generated and by using local map estimation approach, we find the edge configuration and the region intensity for each site in the image. The local results for regions and edges are combined by using Markov Random Field. The clique coefficient of the Markov Random Field which describes our model is estimated by using the "coding method" presented by Besag; a practical method to estimate the Gibbs distribution parameters is to use the histogram method presented by Derin and Elliot. Our approach is unsupervised and the solution to the problems of interest is presented along with experimental results. In addition there is a comparative in the results to the Canny edge detector.