Fuzzy and possibilistic shell clustering algorithms and theirapplication to boundary detection and surface approximation. II

Shell clustering algorithms are ideally suited for computer vision tasks such as boundary detection and surface approximation, particularly when the boundaries have jagged or scattered edges and when the range data is sparse. This is because shell clustering is insensitive to local aberrations, it can be performed directly in image space, and unlike traditional approaches it does assume dense data and does not use additional features such as curvatures and surface normals. The shell clustering algorithms introduced in Part I of this paper assume that the number of clusters is known, however, which is not the case in many boundary detection and surface approximation applications. This problem can be overcome by considering cluster validity. We introduce a validity measure called surface density which is explicitly meant for the type of applications considered in this paper, we show through theoretical derivations that surface density is relatively invariant to size and partiality (incompleteness) of the clusters. We describe unsupervised clustering algorithms that use the surface density measure and other measures to determine the optimum number of shell clusters automatically, and illustrate the application of the proposed algorithms to boundary detection in the case of intensity images and to surface approximation in the case of range images