Real-time color image segmentation based on mean shift algorithm using an FPGA

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Image segmentation is one of the most important tasks in the image processing, and mean shift algorithm is often used for color image segmentation because of its high quality. The computational cost of the mean shift algorithm, however, is high, and it is difficult to realize its real time processing on microprocessors, though many techniques for reducing the cost have been researched. In this paper, we describe an FPGA system for the image segmentation based on the mean shift algorithm. In the image segmentation based on the mean shift algorithm, the image is once over-segmented, and then the small regions are merged considering the similarity between the over-segmented regions to obtain better segmentation. In our system, the mean shift filter is accelerated using a cache memory which can access to all pixels in a w s × w s pixel window at arbitrary position. This cache memory allows us to process w s × w s pixels in parallel every clock cycle. The region merging is also accelerated by not strictly managing the list structures used for the merging. This loose management causes the redundant and out-of-date data into the list structures, but it makes the pointer dereferences unnecessary, and the overhead by those data can be hidden by pipeline processing. The performance for 768 × 512 pixel images is fast enough for real-time applications.