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.
