EDGE-DETECTION IN NUMERICAL IMAGE-PROCESSING

Edge detection takes an important place in image processing and segmentation of remotely sensed data. Its objective is to locate prominent edges in an image, and so to separate the components of an image into subsets that may correspond to the physical objects in the scene. In general, this can be achieved by generating an edge map using, for example, edge detection operators or some threshold techniques. In most cases, these methods assume that at the edge the grey level intensity changes in a discontinuous way (usually as a step function). If we need to segment a textural scene by finding the texture boundaries, traditional methods of edge detection are usually not successful, since they cannot distinguish between the micro-edges within each texture and the boundaries between different textures. One reason for their failure is their inability to characterize properly a texture. This problem can be solved by combining the traditional edge detection techniques with some efficient textural measures. That is, in the edge detection operators, grey levels are replaced by textural features. Recently, a texture spectrum method has been proposed for texture characterization. This Letter presents an example of the application of the texture spectrum to edge detection.