A method that combines region growing and edge detection for image segmentation is presented. The authors start with a split-and merge algorithm wherein the parameters have been set up so that an over-segmented image results. Region boundaries are then eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree in this case). The algorithms were implemented in the C language on a Sun 3/160 workstation running under the Unix operating system. Simple tool images and aerial photographs were used to test the algorithms. The impression of human observers is that the method is very successful on the tool images and less so on the aerial photograph images. It is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise.>

The authors present a method that combines region growing and edge detection for image segmentation. They start with a split-and-merge algorithm where the parameters have been set up so that an oversegmented image results. Then region boundaries are eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree, in this case).>

A postprocessor of the split-and-merge procedure is presented to solve two problems encountered in image segmentation: (1) the proper setting of parameters is difficult and the result may be undergrown (too many regions) or overgrown (too few regions); and (2) the outlines obtained do not lie at edges. The postprocessor carries out two operations, namely, boundary elimination and edge editing, to solve these two problems, respectively. The processing time of these two procedures grows about linearly with the image complexity.>