Object recognition from local scale-invariant features
Citations
274 citations
Cites background or methods from "Object recognition from local scale..."
...While SIFT and SURF are not directly transferable to 3D scans, many of the general concepts, such as the usage of gradients and the extraction of a unique orientation, are useful there....
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...The underlying idea is similar to what is done in SIFT [7] and SURF [2]....
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...Two of the most popular systems for extracting interest points and creating stable descriptors in the area of 2D computer vision are SIFT (Scale Invariant Feature Transform) [7] and SURF (Speeded Up Robust Features) [2]....
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273 citations
273 citations
272 citations
272 citations
Cites background from "Object recognition from local scale..."
...Much of the vision literature is devoted to features that reduce or remove the effects of certain symmetry groups, e.g., [18, 17]....
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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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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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