ScholarWorks@숙명여자대학교

초록

Speeded-up robust features (SURF) is considered to be the most efficient feature extraction algorithm and it has been implemented in powerful hardware for real-time operation due to its characteristics of data-intensive computation of high complexity. Especially the computational load of the descriptor extraction procedure is very significant and the overall performance of SURF can be improved by speeding up the descriptor extraction step with increasing parallel hardware accelerators. However, simply increasing the hardware accelerators is burdensome because of causing significant area and power consumption. Therefore, in this paper, we propose a reconfigurable hardware architecture that enables achieving the maximum performance of the descriptor extraction step with making the best use of the existing accelerators without any additional ones. Experimental results show that the proposed architecture improves the performance of the descriptor extraction step by 21.51% similar to 47.31% with negligible area and small power overheads of less than 1% and 4% when compared with the existing hardware implementations of the SURF algorithm.

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