Abstract: 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.!19
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