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Parallel optimization of O (1) rectangular kernel max/min filters algorithm
Author(s): Tao Hu; Quanyi Chen; Xianyi Ren; Changwei Wang
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Paper Abstract

The max/min filters are the most basic operator in gray-scale morphology. van Herk proposed an efficient O(1) rectangular kernel max/min filters algorithm which is independent of the size of filter window. This paper mainly studies the parallel optimization of van Herk’s max/min filters algorithm by using the single-instruction, multiple data (SIMD) technology and OpenMP parallel optimization technology for multi-core processors. First, the Intel Sreaming SIMD Extensions 2 (SSE2) is used to perform data-level parallel optimization for van Herk’s max/min filters algorithm in column direction, and a method based on histogram statistics is proposed to accelerate the calculation in row direction, the parallel speed-up ratio can reach 2. Then, the OpenMP parallel optimization technology is used to further perform thread-level parallel optimization for the SSE2 optimized version. The total speedup ratio under 4 cores and 4 threads CPU is about 7. Finally, compared with the rectangular kernel gray morphological operators in the powerful machine vision software MVTec HALCON17.12, the optimized algorithm in this paper has a great advantage in large window filtering size.

Paper Details

Date Published: 3 January 2020
PDF: 6 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137330 (3 January 2020); doi: 10.1117/12.2557699
Show Author Affiliations
Tao Hu, Shenzhen Institute of Information Technology (China)
Quanyi Chen, Jiangxi Univ. of Science and Technology (China)
Xianyi Ren, Shenzhen Institute of Information Technology (China)
Changwei Wang, Shenzhen Institute of Information Technology (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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