Graphics processing unit-accelerated multi-resolution exhaustive search algorithm for real-time keypoint descriptor matching in high-dimensional spaces PROJECT TITLE :Graphics processing unit-accelerated multi-resolution exhaustive search algorithm for real-time keypoint descriptor matching in high-dimensional spacesABSTRACT:Image keypoint descriptor matching is an important pre-processing task in various laptop vision applications. This study initial introduces an existing multi-resolution exhaustive search (MRES) algorithm combined with a multi-resolution candidate elimination technique to handle this issue efficiently. A graphics processing unit (GPU) acceleration style is then proposed to enhance 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 initial computes a multi-resolution table of every keypoint descriptor by using a L1-norm-primarily based dimension reduction approach. Next, a fast candidate elimination algorithm is used based mostly on the multi-resolution tables to get rid of 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 economical, but it is terribly suitable for parallel implementation on the GPU. Therefore, this study presents a GPU acceleration technique for the MRES algorithm to attain better real-time performance. Experimental results validate the computational potency and matching accuracy of the proposed algorithm by comparing with three existing methods. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Unfolding Dynamic Networks for Visual Exploration A Survey of Security in Software Defined Networks