Edge detection in textures

Abstract

Publisher Summary This chapter discusses the problem of detecting edges in cellular textures. Detecting edges is an important first step in the solution of many image analysis tasks. Edges are used primarily to aid in the segmentation of an image into meaningful regions, but they are also extensively used to compute relatively local measures of textural variation. Once edges are detected in textured regions, they can be used to define texture descriptors in a variety of ways. A general edge detection procedure may involve applying an edge-sensitive operator to the texture, thresholding the results of the edge operator, and finally computing peaks from the above-threshold points. Subsequently, one can compute first-order statistics of edge properties, such as orientation, contrast, and fuzziness, or higher-order statistics that can measure the spatial arrangement of edges in the texture. Such statistics can be computed from generalized co-occurrence matrices, which count the number of times that specific pairs of edges occur in specific relative spatial positions. The utility of such tools depends on the reliability with which edges can be detected in textures.