Object recognition from local scale-invariant features
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...– Modified SIFT (Lowe 1999) and intensity domain SPIN images (Lazebnik et al. 2003; Zhang et al. 2006)....
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...– Modified SIFT (Lowe 1999) and intensity domain SPIN images (Lazebnik et al....
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211 citations
Cites methods from "Object recognition from local scale..."
...ages. To address these issues, many representation learning methods have been proposed for image feature extractions as a preprocessing step. Traditionally, various hand-crafted features such as SIFT [11], HOG [12], NMF [13], and (geometric) CW-SSIM similarity [14, 15] have been used to encode the visual information. Recently, many approaches have been proposed to combine clustering methods with deep ...
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References
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"Object recognition from local scale..." refers background or methods in this paper
...This allows for the use of more distinctive image descriptors than the rotation-invariant ones used by Schmid and Mohr, and the descriptor is further modified to improve its stability to changes in affine projection and illumination....
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...For the object recognition problem, Schmid & Mohr [19] also used the Harris corner detector to identify interest points, and then created a local image descriptor at each interest point from an orientation-invariant vector of derivative-of-Gaussian image measurements....
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..., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations....
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...However, recent research on the use of dense local features (e.g., Schmid & Mohr [19]) has shown that efficient recognition can often be achieved by using local image descriptors sampled at a large number of repeatable locations....
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...[23] used the Harris corner detector to identify feature locations for epipolar alignment of images taken from differing viewpoints....
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