Mosaic image method: a local and global method

摘要

In this paper, a new method to compute eigenimages in principal component analysis (PCA) based vision systems is presented. It is called the 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 the resulting representation is better than that obtained using the traditional optimum representation. A simple algorithm for recognition based on the new representation is proposed. Extensive experiments are conducted. More than 110,000 test images with varying degree of occlusion are used to test the proposed method. It is found that the new method can accommodate up to 53% occluded parts with a recognition rate of more than 95%. To our knowledge, this is the best result in the presence of occlusion in PCA-based vision systems.