A Parallel and Memory-Efficient Mean Shift Filter on a Regular Graph

Toward real-time mean shift, a high-speed and parallel mean shift filter on a 2D regular graph is presented in this paper. For an by image and with iteration times, ´ µ time complexity of sequential computation is re- duced to ´ µ with processors, and ´ µ memory complexity is reduced to ´ µ when is smaller than . As a result, computational speed is improved by using cas- caded parallel processors. Furthermore, the proposed filter is adequate for VLSI implementation due to a linear sys- tolic array structure. In this paper, we present quantitative and qualitative experimental results by using images in The Berkeley Image Segmentation Dataset. The proposed par- allel algorithm requires 6 times smaller data access range and 2 times smaller memory size than the standard mean shift filtering at 15 iterations.