Image Segmentation on IRAM
نویسندگان
چکیده
The Computer Vision group at U.C. Berkeley recently developed a novel approach to image segmentation, called the Normalized Cuts algorithm. The current implementation of the algorithm has an execution time on the order of minutes for medium-sized images running on conventional scalar machines. This paper explores the current bottlenecks and seeks to maximize the performance by porting the algorithm to the new IRAM vector architecture and vectorizing the critical routines. Simulation results are presented demonstrating the execution time of the streamlined algorithm to be over 6 times faster than the scalar version.
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تاریخ انتشار 2007