Image screening using spatial filters based on linear transformation for image recognition

Statistical image recognition methods based on linear transformation require a lot of calculation of correlation between subimages and reference patterns of the specified objects to be detected. Image screening provides an effective preprocessing method for lowering the calculation load and improving recognition accuracy. It selects candidate subimages that are similar to the detecting objects in images and rejects the remainders using spatial filters which are based on linear transformation. We have already investigated the spatial filters that are based on 2D projection pursuit (PP). PP requires more heavy calculation load than the principal components analysis (PCA). We, therefore, compare spatial filters based on two kinds of linear transformation algorithms, the PP and PCA, in terms of recognition accuracy and efficiency. Experiments are made for two object detection tasks: eye- and mouth-area detection in face images and text-area detection in document images. The results show that PCA-based image screening is superior to PP-based one for the eye- and mouth-area detection. PCA also achieves higher recognition rate (75%) than PP for the eye- and mouth-area detection, while PP offers equal performance in text-area detection. The results suggest that PCA is totally superior to PP in image screening.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.