TR-A-0113 :1991.4.23

Ed Gamble

Simplifying Discontinuity Detection with an Eye on Recognition

Abstract:The essence of our approach is to address the important problem of discontinuity detection within the context of the overall visual recognition problem. Awareness of the capabilities and expectations embodied in recognition algorithms and of the dominant noise process in the surface properties computed by some early vision algorithms greatly simplifies the detection of discontinuity. In particular, we describe the characteristic “displacement” errors, that stereo and optical-flow algorithms produce near object boundaries and we suggest that the detected discontinuities, in light of these errors, must be restricted to a subset of intensity edges. This restriction simplifies discontinuity detection and is valid under certain assumptions which we describe. We have detected discontinuities in depth and in the magnitude of optical flow for a variety of natural images by combining intensity edges and surface property data computed with early vision algorithms. The integration of surface properties is formulated as an optimization problem derived from a Markov random field. A massively parallel, stochastic relaxation algorithm for solution of these optimization problems is described.