Object recognition from local scale-invariant features
Citations
980 citations
Cites background or methods from "Object recognition from local scale..."
...The usability of randomly positioned imagery is based on progress in the area of automated image matching (e.g. the scale invariant feature transform (SIFT) of Lowe, 1999)....
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...KEYWORDS: structure from motion; topographic modeling; digital elevation models; LiDAR...
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...In contrast, algorithms such as the SIFT key developed by Lowe (1999) rely onmultiscale image brightness and colour gradients in order to identify points in the image which can reliably be identified as conjugate....
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980 citations
Cites methods from "Object recognition from local scale..."
...The local descriptors are fixed in all experiments to be SIFT descriptors [13] extracted with a spatial stride of between two and five pixels, and at four scales, defined by setting the width of the SIFT spatial bins to 4, 6, 8 and 10 pixels respectively....
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975 citations
Cites background or methods from "Object recognition from local scale..."
...Lowe’s SIFT descriptor [ 17 ] [18] have been shown in various studies e.g....
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...Leung et al. [16], Schmid and Mohr [28], and Lowe [ 17 ] additionally use gray level information at the keypoints to provide greater discriminative power....
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971 citations
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969 citations
Cites background from "Object recognition from local scale..."
...Interest point detectors (IPs) [20, 27] respond to local textured image neighborhoods and are widely used for finding image correspondences [27] and recognizing specific objects [24]....
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References
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1,756 citations
"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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