Boundary detection using multiscale Markov random fields
The basic difficulty encountered in filtering-based multiscale boundary detection methods is the elimination of noise and insignificant edges while preserving positional accuracy at the image discontinuities. In this paper, a nonlinear multiscale boundary detection method which prevents the conflict between the detection and localization goals is introduced. The method uses multiscale representations of coupled Markov random fields and applies a stochastic regularization scheme based on the Bayesian approach. This allows the integration of boundary information extracted at multiple scales simultaneously resulting in robust integration of the information at a variety of spatial scales. The scheme is applicable to intensity images as well as to range images and eliminates the dependency on edge operator size which is the main difficulty in filtering-based multiscale techniques.