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• W1997184617 abstract "Scale-invariant feature transform (SIFT) algorithm, one of the most famous and popular interest point detectors, detects extrema by using difference-of-Gaussian (DoG) filter which is an approximation to the Laplacian-of-Gaussian (LoG) for improving speed. However, DoG filter has a strong response along edge, even if the location along the edge is poorly determined and therefore is unstable to small amounts of noise. In this paper, we propose a novel interest point detection algorithm, which detects scale space extrema by using a Laplacian-of-Bilateral (LoB) filter. The LoB filter, which is produced by Bilateral and Laplacian filter, can preserve edge characteristic by fully utilizing the information of intensity variety. Compared with the SIFT algorithm, our algorithm substantially improves the repeatability of detected interest points on a very challenging benchmark dataset, in which images were generated under different imaging conditions. Extensive experimental results show that the proposed approach is more robust to challenging problems such as illumination and viewpoint changes, especially when encountering large illumination change." @default.
• W1997184617 created "2016-06-24" @default.
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• W1997184617 date "2015-01-01" @default.
• W1997184617 modified "2023-10-15" @default.
• W1997184617 title "A Novel Approach for Interest Point Detection via Laplacian-of-Bilateral Filter" @default.
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• W1997184617 doi "https://doi.org/10.1155/2015/685154" @default.
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