Introduction

In some applications such as feature detection, the initial step before the detection is the segmentation of the image into various regions to separate the feature from the background. This procedure is commonly referred to as image segmentation. Depending on whether there are single or multiple features, the result is a partition of the image into a certain number of homogeneous regions. Each pixel element of the image is assigned to one of the homogeneous regions. Some criteria of region homogeneity are usually gray level intensity, color, texture, etc. Hence, image segmentation can be regarded as scene classification with respect to some criteria. The process is complicated most of the time by essentially two problems: the nonuniformity of the gray level intensity of the image feature regions and the loss of contrast in some of the regions.

A popular approach to segmentation is based on region growing, which involves the merging of small uniform regions to form large regions without the uniformity of the combined regions being violated. The result of the merging process in this case depends on a suitable uniformity criterion. Some techniques in this area are based on estimation theory. The region-based segmentation procedures are classified into three basic categories: pure splitting, pure merging, and split-and-merge.