Object recognition from local scale-invariant features
Citations
473 citations
472 citations
Cites background from "Object recognition from local scale..."
...Shape representations in terms of histograms of image gradients have shown excellent performance on object recognition problems [4, 11, 13]....
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467 citations
Cites background or methods from "Object recognition from local scale..."
...extended this approach to incorporate scale invariance [ Low99 , Low04]....
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...features based matching have been demostrated for object recognition [ Low99 ], structure...
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464 citations
Cites methods from "Object recognition from local scale..."
...These are equivalent to Lowe’s [27] SIFT and the DPM V5 HOG [11, 14]....
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...These are equivalent to Lowe’s [24] SIFT and the DPM V5 HOG [10, 13]....
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...Popularized by Lowe in [24] with the SFIT descriptor, here we consider two densely-sampled versions [30], namely DSIFT (Dense SIFT) and HOG [5]....
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460 citations
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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1,574 citations
"Object recognition from local scale..." refers methods in this paper
...[23] used the Harris corner detector to identify feature locations for epipolar alignment of images taken from differing viewpoints....
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