An evidential reasoning approach for recognizing shape features

The paper introduces an evidential reasoning based approach for recognizing and extracting manufacturing features from solid model description of objects. A major difficulty faced by previously proposed methods for feature extraction has been the interaction between features. In interacting situations, the representation for various primitive features is non-unique, making their recognition very difficult. We develop an approach based on generating and combining geometric and topological evidences for recognizing interacting features. The essence of our approach is in finding a set of correct and necessary virtual links through aggregating the available geometric and topologic evidences at different abstraction levels. The identified virtual links are then augmented to the cavity graph representing a depression of an object so that the resulting supergraph can be partitioned to obtain the features of the object. The main contributions of our approach include introducing the evidential reasoning (Dempster-Shafer theory) to the feature extraction domain and developing the theory of principle of association to overcome the mutual exclusiveness assumption of the Dempster-Shafer theory