Multiple descent cost competitive learning and data-compressed 3-D morphing

Multiple descent cost competitive learning is applied to data-compressed texture generation for 3D image processing and graphics. This learning method organizes itself by generating two types of feature maps: the grouping feature map and the weight vector feature map, which can both change regional shapes. This merit makes it possible for users to generate data-compressed image morphing. The resulting textures can be used to create virtual 3D objects. Examples are given of generating emotional expressions. The theoretical relationship between the /spl alpha/-EM (expectation maximization) algorithm and the multiple descent cost competitive learning algorithm is also clarified.