Object recognition from local scale-invariant features
Citations
164 citations
Cites background from "Object recognition from local scale..."
...In addition to less demanding computational requirements, in principle, distinctive features invariant to scale, rotation illumination variations, and/or 3D projection can be extracted for fast object detection (Lowe, 1999) and thus it is quite desirable for the task of particle selection....
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164 citations
Cites background from "Object recognition from local scale..."
...We compare our object bank features to SIFT-BoW, GIST, and SPM....
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...The GIST and SIFT-SPM representations often show similar distributions for such images....
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...SIFT [29], filterbanks [14, 34], GIST [33], etc....
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...The maximum number of images in those classes is 1000. other hand, the combination of SIFT and GIST features does not improve the classification performance, suggesting that these two low-level representations are largely similar to each other, offering little complementary information for further discrimination of the scenes....
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...2 Related work A plethora of image feature detectors and descriptors have been developed for object recognition and image classification [33, 1, 22, 30, 29]....
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164 citations
Cites background from "Object recognition from local scale..."
...This work has found wide application in areas such as object recognition [5, 13], wide baseline matching [19, 23], and image retrieval [20]....
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164 citations
164 citations
Cites background or methods from "Object recognition from local scale..."
...SIFT keypoint generation is fast, as claimed in Lowe’s work [28] that 1000 SIFT keys generation requires less than 1 second of computation time....
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...SIFT is traditionally used by computer vision applications to recognize objects in cluttered, real world scenes [28]....
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...To extract features from a site logo, Scale Invariant Feature Transform (SIFT) algorithm [28] is used....
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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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