The basic difficulty encountered in filtering-based multiscale boundary detection methods is the elimination of noise and insignificant edges without distorting the shape of boundaries, These methods remove noise and unnecessary detail by blurring the input image at different scales, which results in the loss of 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 robust integration of boundary information extracted at multiple scales simultaneously. The scheme is applicable to intensity and range images as well as to sparse data and eliminates the dependency on edge operator size which is the main difficulty in filtering-based multiscale techniques. (C) 1996 Academic Press, Inc.