Image segmentation: A comparative study

Machine extraction of meaningful features from the digitized representation of an image (picture, scene etc.) is of great interest to investigators working in such diverse fields as robotic vision, scene analysis, pattern recognition, and automatic part identification in manufacturing processes. The authors describe in detail their algorithms for implementing different segmentation strategies. These are a label propagation segmentation scheme (using the region growing algorithm) and a linked pyramid segmentation scheme. The two techniques are analyzed and compared with respect to their ability to satisfactorily segment a wide class of images (scenes, radiographs, machine parts etc.); computational overheads; memory overheads; and sensitivity to additive noise (Gaussian). In addition to the critical analysis and evaluation of the two techniques, the authors introduce the following enhancements: new predicates (similarity criteria) that are applicable to a broad class of images; incorporation of impulse noise suppression; hierarchical two-level processing to refine segmentation by label propagation; and use of a weighting function to improve the segmentation process.