Curvature-based fuzzy surface classification
In this paper, a fuzzy surface classification paradigm, which is an extension to the conventional techniques based on the sign of the mean (H) and Gaussian (K) curvatures, respectively is presented. With the conventional methods, two of the major problems that limit object descriptions are: 1) Their, inability to describe surfaces in a natural way, and 2) computation of curvatures being highly sensitive to noise as well as limited by resolution. Problem 1) is addressed by treating the transitional regions between distinct surface types as smoothly varying (fuzzy) surface types. Problem 2) gets partially resolved while fuzzifying the signs of the surface curvatures for surface description. The new segmentation technique is demonstrated in a model-based object recognition system and its performance is compared with a system based on conventional surface classification.
curvature measures fuzzy logic range image segmentation three-dimensional (3-d) object recognition