文献类型：期刊文献

英文题名：Parallel Harris Corner Detection on Heterogeneous Architecture

作者：He, Yiwei[1]; Ma, Yue[2]; Liu, Dalian[3]; Chen, Xiaohua[4]

第一作者：He, Yiwei

通讯作者：Liu, Dalian

机构：[1] School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China; [2] School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China; [3] Department of Basic Course Teaching, Beijing Union University, Beijing, China; [4] Dean’s office, Beijing Union University, Beijing, China

第一机构：School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China

年份：2018

卷号：10861 LNCS

起止页码：443-452

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20182605384885)

语种：英文

外文关键词：Object detection - Parallel architectures - Edge detection - Object recognition

摘要：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. ? Springer International Publishing AG, part of Springer Nature 2018.