蜂窝宽带引擎结构中尺度不变关键点检测算法的并行化

Bomjun Kwon, Tai-Ho Choi, Heejin Chung, Geonho Kim
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引用次数: 7

摘要

本文描述了一种从数字图像中检测尺度不变关键点的并行算法的设计和实现。关键点检测是图像处理中最重要的操作之一,因为它可以实现有效的图像匹配。图像匹配在许多智能图像处理服务中都有应用,包括物体/场景识别、立体对应、运动跟踪和全景成像。本文通过将关键点检测算法的每个子过程转换为并行版本,设计了一种新的适用于蜂窝宽带引擎架构的关键点检测并行算法。实验结果表明,该算法的性能与所使用的处理器数量成正比,即实现了线性可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallelization of the Scale-Invariant Keypoint Detection Algorithm for Cell Broadband Engine Architecture
This paper describes design and implementation of a parallel algorithm that detects scale-invariant keypoints from digital images. Keypoint detection is one of the most important operations in image processing since efficient image matching is possible with it. Image matching is used in many intelligent image processing service, including object/scene recognition, stereo correspondence, motion tracking, and panorama imaging. In this paper, we design a new parallel algorithm of keypoint detection that is suitable for Cell Broadband Engine architecture by converting each subprocess of keypoint detection algorithm into parallel version. The experimental results show that performance of our algorithm increases in proportion to the number of processors utilized, that is, it achieves linear scalability.
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