Adaptive Filters for Edge Detection

Abstract

Edge detection in machine vision usually consists of filtering the image with a set of circularly symmetric and/or even and odd symmetric oriented filters covering a range of spatial scales. The filters' responses at each point in the image are then thresholded either before or after being combined in some manner. Selecting functions to combine responses of filters with differing spatial scales, orientations, and symmetries is a major problem with this type of approach, as is choosing appropriate thresholds. Additionally, the computational burden has rendered the approach unfit for most practical image processing systems at this time. A new "constrained matched filter" algorithm is presented which addresses these problems. At each pixel, the algorithm computes a consistency measure and forms a template based on simple measurements of changes in intensity gradient in a small neighbourhood. Consistency is a measure of the localization of gradient changes within the neighbourhood. The location of a possible edge pixel, which need not coincide with the template center, is determined. The template is cross-correlated with the image, and the result is accumulated in an output image at the edge-pixel location previously found. The result image may be thresholded to generate a "line drawing" showing the locations of lines, step edges and roof edges.