Composite edge detection with random field models

A pixel is regarded as an edge pixel if there is either a sharp change in the intensity values on either side of the pixel like a step function, yielding a so-called step edge or the texture on either side of the pixel is different, yielding a so-called texture edge. A two stage generate-and-confirm paradigm for detecting all the edge pixels in the scene is used. In the first stage, a directional derivatives approach for determining all potential edge pixels and the direction of the edge is employed. At this stage some of the edge pixels could be spurious, typically caused by either the noise in the image or the microedges inside a texture. In the second stage, each candidate pixel is subjected to two separate tests to confirm whether the edge pixel is a step edge or texture edge. The texture edge is confirmed by a likelihood-ratio test. The likelihood function is computed by fitting a nonsymmetric half-plane random-field model to the texture in a rectangular strip where the dominant direction is perpendicular to the estimated edge direction. Only the edge pixels that pass at least one of the two tests is accepted. The validity of the method is demonstrated by testing four different images