This paper addresses the general 3-D rigid motion problem, where the point correspondences and the motion parameters between two sets of 3-D points are to be recovered. The existence of missing points in the two sets is the most difficult problem. We first show a mathematical symmetry in the solutions of rotation parameters and point correspondences. A closed-form solution based on the correlation matrix eigenstructure decomposition is proposed for correspondence recovery with no missing points. Using a heuristic measure of point pair affinity derived from the eigenstructure, a weighted bipartite matching algorithm is developed to determine the correspondences in general cases where missing points occur. The use of the affinity heuristic also leads to a fast outlier removal algorithm, which can be run iteratively to refine the correspondence recovery. Simulation results and experiments on real images are shown in both ideal and general cases.

We present a method of indexing color images based on color surface reflectance classes. Specifically, we analyse the distributions from members of a surface color class in a chrominance-based color space and show that they form collinear clusters under identical illumination. We also design an optimal color space based on the Fisher discriminant criterion to maximally separate members of different color surface classes. Finally, color indexing is achieved by finding regions in unsegmented images that belong to a queried color surface class using the location and orientation information of the color distributions in the optimal color space. Results are shown that indicate that using both location and orientation information of surface color in the optimal color space lowers the number of false positives and negatives in indexing.

A system has been developed to acquire, extend and refine 3D geometric site models from aerial imagery. This system hypothesizes potential building roofs in an image, automatically locates supporting geometric evidence in other images, and determines the precise shape and position of the new buildings via multiimage triangulation. Model-to-image registration techniques are applied to align new, incoming images against the site model. Model extension and refinement procedures are then performed to add previously unseen buildings and to improve the geometric accuracy of the existing 3D building models.>

The feature image and projective image are first proposed to describe the human face, and a new method for human face recognition in which projective images are used for classification is presented. The projective coordinates of projective image on feature images are used as the feature vectors which represent the inherent attributes of human faces. Finally, the feature extraction method of human face images is derived and a hierarchical distance classifier for human face recognition is constructed. The experiments have shown that the recognition method based on the coordinate feature vector is a powerful method for recognizing human face images, and recognition accuracies of 100 percent are obtained for all 64 facial images in eight classes of human faces.>

This paper presents a new method for computing the discriminant vectors of the Foley–Sammon optimal set. First, an equivalent criterion is presented to replace the Fisher criterion; then, the problem of computing the discriminant vectors in Rn is transformed into the maximum problem in a subspace. Several theorems relating to the method are also presented. Experimental results show that the present method is superior to the positive pseudoinverse method, and the perturbation method in terms of correct classification rate.