Shadow image edge detection by using an adaptive background model is a critical component for many vision-based applications. Most background models were maintained in pixel-based forms, while some approaches began to study block-based representations which are more robust to non-stationary backgrounds. In this paper, a novel method that combines edge growing and granular computing approaches into a single framework is proposed. Efficient hierarchies can be built with these two approaches complementary to each other. In addition, a novel model is proposed for shadow edge using edge growing from the edge nodes in the coarse level of the hierarchy. As can be seen from the experimental analysis, the method we proposed has better performance than existing single-level approaches in edge detection and image segmentation.
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