Aiming at the urgent requirement of rapid and efficient 3D model shape analysis and retrieval technology, a new method of 3D model shape correspondence analysis by combining the intrinsic heat kernel features and local volume features was proposed. Firstly, the intrinsic shape features of the model were extracted by using Laplacian Eigenmap and heat kernel signature. Then, the feature matching matrix was established by combining the stability of the model heat kernel feature and the significance of local space volume. Finally, the model registration and shape correspondence matching analysis was implemented through feature matrix similarity measurement and short path searching. The experimental results show that, the proposed shape correspondence analysis method with the combination of heat kernel distance and local volume constraint can not only effectively improve the efficiency of model shape matching, but also identify the structural features of the same class models. The proposed method can be applied to further realize the co-segmentation and shape retrieval of multigroup models.

Evidence theory is one of the main approaches to implement situation assessment based on rules. But evidence theory can result in paradox problem in conflicting evidence combination. Concerning this problem, by dissimilarity calculation, the importance of evidence was measured and original evidence was modified. A new approach based on the improved evidence theory was proposed. The new approach contained four steps including rules measurement, evidence modification, rules fusion and situation decision. The experiments show that the new approach can avoid paradox problem in the process of fusion based on evidence theory, and it is superior to typical approaches, such as Dempster approach, Yager approach and Leung approach, etc, in efficiency and accuracy of situation assesment.