Parallel optimization of O (1) rectangular kernel max/min filters algorithm

Tao Hu; Quanyi Chen; Xianyi Ren; Changwei Wang

doi:10.1117/12.2557699

3 January 2020 Parallel optimization of O (1) rectangular kernel max/min filters algorithm

Tao Hu, Quanyi Chen, Xianyi Ren, Changwei Wang

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137330 (2020) https://doi.org/10.1117/12.2557699
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

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.

Citation Download Citation

Tao Hu, Quanyi Chen, Xianyi Ren, and Changwei Wang "Parallel optimization of O (1) rectangular kernel max/min filters algorithm", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137330 (3 January 2020); https://doi.org/10.1117/12.2557699

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available