Mosaic image method: a local and global method

In this paper, a new method to compute eigenimages in principal component analysis (PCA) based vision systems is presented. It is called Mosaic Image Method. In this method, the object is represented as a collection of features and their relative positions (topology). This is a local and global method. Although this method is created to account for the occlusion problem, it is found that this is a better representation in general than the traditional optimum representation. A simple algorithm based on the new representation is proposed for recognition. Thorough experiments are conducted. More than 110,000 test images with diierent degree of occlusion are used to test the proposed method. The new method can accommodate up to 53% occluded parts with a more than 95% correct recognition rate. To the authors' knowledge, this is the best result in the presence of occlusion in PCA-based vision systems.