Context dependent edge detection and evaluation

Abstract

To simulate the edge perception ability of human eyes and detect scene edges from an image, context information must be employed in the edge detection process. To accomplish the optimal use of context, we introduce an edge detection scheme which uses the context of the whole image. The edge context for each pixel is the set of all row monotonically increasing paths through the pixel. The edge detector assigns a pixel that edge state having highest edge probability among all the paths. Based on the same framework, we developed a general robust evaluator for edge detectors. The scheme, based on local edge coherence, does not require any prior information about the ideal edge image and allows any size of neighborhood with which local edge coherence is evaluated on the basis of continuity, thinness, and positional accuracy. The edge evaluator can be incorporated with a feedback mechanism to automatically adjust edge detection parameters (e.g. edge thresholds), for adaptive detection of edges in real images. Experiments indicate the validity of the edge detector and the general edge evaluator. Upon comparing the performance of the context dependent edge detector with the context free second directional derivative zero-crossing edge operator, we find that the context dependent edge detector is superior.