To simulate the edge perception ability of human eyes and detect scene edges from an image, context information and world constraints must be employed in the edge detection process. To accomplish this, two Bayesian decision theoretic frameworks for context dependent edge detection are developed around the local facet edge detector. The first approach uses all the context in the neighborhood of a pixel. The second approach uses the context of the whole image. The mechanism of the context edge detector then assigns a pixel the most probable edge state which is consistent with its assumed edge context.

We also demonstrate how world constraints can aid the edge detection process with a lighting compensation and a curvature constraint scheme. The context information and world constraints can also be used to evaluate the performance of different edge detectors. A general edge coherence measure, a robust edge thinness measure, and a general edge correctness measure are developed.

Upon comparing the performance of the edge detectors with the context free second directional derivative zero-crossing edge operator, we find that the context dependent edge detector is superior; the world constrained context free edge detectors can also improve the edge result.

Finally, some simple edge detection schemes based on morphologic operations are discussed and evaluated. Although their performances are not as good as the other edge operators described in this dissertation, they are acceptable in the images which have reasonably high signal-to-noise ratio. These morphological edge operations can be realized most efficiently in machine vision systems that have special hardware designed for morphologic operations.