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Keypoint matching between images is an important technique for computer vision applications such as image retrieval. Although binary feature descriptors such as BRIEF enable fast measurement of distance, exhaustive search is still time-consuming. Hashing methods such as Locality Sensitive Hashing (LSH), while being effective to accelerate searching, result in large memory consumption and thus are...
We propose and demonstrate an FPGA-accelerated partial-image-matching engine for massive media-data searching systems. To take advantage of FPGA, a highly parallelized and pipelined architecture with an application-specific calculation was adopted. Our prototype system achieves 32 times better runtime performance than a CPU-based solution.
In this paper, we introduce an FPGA-accelerated partial image retrieval engine, suitable for a visualized document search system. To achieve efficient sharing and reuse of digitized documents, this system has the function of partial duplicate image retrieval. To meet the demand for stability and speed, we introduced a brute-force matching of the BRIEF descriptor with two step scheme and an FPGA accelerator...
When a farmer shares information about farm situation or events with other farmers, he often uses pictures (images) taken during farm inspection for visual communication. However, it may take much time for the farmers to find the desired images from a large amount of images accumulated every day. Furthermore, the contents of the images are diverse (e.g. crop images, soil images, and field images),...
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