A GPU based implementation of Center-Surround Distribution Distance for feature extraction and matching

Aditi Rathi, M. DeBole, Weina Ge, R. Collins, N. Vijaykrishnan
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引用次数: 1

Abstract

The release of general purpose GPU programming environments has garnered universal access to computing performance that was once only available to super-computers. The availability of such computational power has fostered the creation and re-deployment of algorithms, new and old, creating entirely new classes of applications. In this paper, a GPU implementation of the Center-Surround Distribution Distance (CSDD) algorithm for detecting features within images and video is presented. While an optimized CPU implementation requires anywhere from several seconds to tens of minutes to perform analysis of an image, the GPU based approach has the potential to improve upon this by up to 28X, with no loss in accuracy.
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基于GPU的中心环绕分布距离特征提取与匹配实现
通用GPU编程环境的发布已经获得了曾经只有超级计算机才能获得的通用计算性能。这种计算能力的可用性促进了新算法和旧算法的创建和重新部署,创造了全新的应用程序类别。本文提出了一种中心环绕分布距离(CSDD)算法的GPU实现,用于检测图像和视频中的特征。虽然优化的CPU实现需要从几秒到几十分钟的时间来执行图像分析,但基于GPU的方法有可能在此基础上提高高达28倍,而精度没有损失。
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