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Jie Chen; Ling-Yu Duan; Feng Gao; Jianfei Cai; Kot, Alex C.; Tiejun Huang
IEEE signal processing letters, 2015-Feb., 2015-2-00, 20150201, Volume: 22, Issue: 2Journal Article
Interest point detection is a fundamental approach to feature extraction in computer vision tasks. To handle the scale invariance, interest points usually work on the scale-space representation of an image. In this letter, we propose a novel block-wise scale-space representation to significantly reduce the computational complexity of an interest point detector. Laplacian of Gaussian (LoG) filtering is applied to implement the block-wise scale-space representation. Extensive comparison experiments have shown the block-wise scale-space representation enables the efficient and effective implementation of an interest point detector in terms of memory and time complexity reduction, as well as promising performance in visual search.
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