Contour extraction of images on parallel computer-local, parallel and stochastic algorithm which learns energy parameters

The stochastic relaxation algorithm for S. Geman and D. Geman (1984), which is based on the Markov random-field (MRF) model of images, was implemented on a hypercube parallel computer for contour extraction of human face images. The local energy parameters that define the MRF model were estimated by the authors´ learning algorithm (1989), while supervised by a desirable object contour as a teaching signal. D. Geman´s constrained optimization method (1987) was utilized to avoid flaws in a fast simulated annealing. The contours extracted by the learning stochastic relaxation method were systematically compared with those obtained by several conventional edge detection methods. The contours extracted by the authors´ method include few discontinuity points and small amounts of noise, and faithfully represent the true contours. The authors propose an algorithm, multiple-level, multiple-resolution MRF, which is an extension of the original MRF. This model can incorporate a priori knowledge about the global structures in images and still be implemented in a local and parallel mode.<>