Parallel Harris Corner Detection on Heterogeneous Architecture

  • Yiwei He
  • Yue Ma
  • Dalian Liu
  • Xiaohua Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)


Corner detection is a fundamental step for many image processing applications including image enhancement, object detection and pattern recognition. Recent years, the quality and the number of images are higher than before, and applications mainly perform processing on videos or image flow. With the popularity of embedded devices, the real-time processing on the limited computing resources is an essential problem in high-performance computing. In this paper, we study the parallel method of Harris corner detection and implement it on a heterogeneous architecture using OpenCL. We also adopt some optimization strategy on the many-core processor. Experimental results show that our parallel and optimization methods highly improve the performance of Harris algorithm on the limited computing resources.


Harris corner detection Heterogeneous architecture Parallel computing OpenCL 



This work has been partially supported by grants from the National Natural Science Foundation of China (Nos. 61472390, 71731009, 71331005 and 91546201), the Beijing Natural Science Foundation (No. 1162005), Premium Funding Project for Academic Human Resources Development in Beijing Union University.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Basic Course TeachingBeijing Union UniversityBeijingChina
  4. 4.Dean’s officeBeijing Union UniversityBeijingChina

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