Building meaningful edge maps from range images

Edge detection in range images provides rich information about the scene being observed. Many researchers have been taking advantage of this information to improve image segmentation by integrating edge detection with other segmentation techniques. This paper presents a methodology to perform edge detection in range images in order to provide a reliable and meaningful edge map, which helps to guide and improve range image segmentation by clustering techniques. The obtained edge map leads to three important improvements: (1) the definition of the ideal number of clusters to initialise the clustering algorithm; (2) the selection of suitable initial cluster centres; and (3) the successful identification of distinct regions with similar features. Experimental results that substantiate the effectiveness of this work are presented.