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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.

This paper describes how motion segmentation can be achieved by analyzing of a single static image that is created from a series of picture frames. The key idea is motion imaging; in other words, motion is expressed in static images by integrating, frame after frame, the spatiotemporal fluctuations of the gradient gray level at each local area. This tends to create blurred or attached line images (images with lines that show the path of movement of an object through space) on moving objects. We call this 'motion texture'. We computed motion texture images based on the animation of a natural scene and on a number of computer synthesized animations containing groups of moving objects (random dots). Moreover, we applied two different texture analyses to the motion textured images for segmentation: a texture analysis based on the local homogeneity of gray level gradation in similarly textured regions and another based on the structural feature of gray level gradation in motion texture. Experiments showed that subjective visual impressions of segmentation were quite different for these animations. The texture segmentation described here successfully grouped moving objects coincident to subjective impressions. In our random dot animations, the density of the basic motion vectors extracted from each pair of successive frames was set at a constant to compensate for the dot grouping effect based on the vector density. The dot appearance period (lifetime) is varied across the animations. In a long lifetime random dot animation, region boundaries can be more clearly perceived than in a short one. The different impressions may be explained by analyzing the motion texture elements, but can not always be represented successfully using the motion vectors between two successive frames whose density is set at a constant between the animations with the different lifetime.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.