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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106170


    Title: Graphics processing unit-accelerated multi-resolution exhaustive search algorithm for real-time keypoint descriptor matching in high-dimensional spaces
    Authors: Chi-Yi Tsai;Chih-Hung Huang;An-Hung Tsao
    Date: 2016/03/11
    Issue Date: 2016-04-22 13:22:53 (UTC+8)
    Publisher: The Institution of Engineering and Technology
    Abstract: Image keypoint descriptor matching is an important pre-processing task in various computer vision applications. This study first introduces an existing multi-resolution exhaustive search (MRES) algorithm combined with a multi-resolution candidate elimination technique to address this issue efficiently. A graphics processing unit (GPU) acceleration design is then proposed to improve its real-time performance. Suppose that a scale-invariant feature transform like algorithm is used to extract image keypoint descriptors of an input image, the MRES algorithm first computes a multi-resolution table of each keypoint descriptor by using a L1-norm-based dimension reduction approach. Next, a fast candidate elimination algorithm is employed based on the multi-resolution tables to remove all non-candidates from a candidate matching list by using a simple L1-norm computation. However, when the MRES algorithm was implemented on the central processing unit, the authors observed that the step of multi-resolution table building is not computationally efficient, but it is very suitable for parallel implementation on the GPU. Therefore, this study presents a GPU acceleration method for the MRES algorithm to achieve better real-time performance. Experimental results validate the computational efficiency and matching accuracy of the proposed algorithm by comparing with three existing methods.
    Relation: IET Computer Vision 10(3), pp.212-219
    DOI: 10.1049/iet-cvi.2015.0137
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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