Object recognition from local scale-invariant features
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Cites methods from "Object recognition from local scale..."
...We also experimented with the SIFT (Scale-Invariant Feature Transform) algorithm, which detects and matches interesting features in images while preserving invariance constraints for scaling, translation, and rotation [15]....
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99Ā citations
Cites methods from "Object recognition from local scale..."
...The model used for testing is trained with a 1-versus-1 SVM....
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99Ā citations
Cites methods from "Object recognition from local scale..."
...We used SIFT [19] as local descriptor and local kernels [20] for SVM....
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99Ā citations
Cites background from "Object recognition from local scale..."
...HoG [15], SIFT [27]) from the neighbourhood around location `i....
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...F denotes a feature vector extraction function (e.g. HoG [15], SIFT [27]) from the neighbourhood around location `i. ā S(s|I,m) denotes the deformation cost when all the adjacent parts (vmi , v m j ) : i, j ā Em are placed in lo- cations `i and `j respectively. dxij = xi ā xj and dyij = yi ā yj are the relative locations (displacements) of the i-th part with respect to the j-th one....
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...The number of appearance components (nA) is 50 and 100 respectively and dense SIFT features were used....
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99Ā citations
Cites background or methods from "Object recognition from local scale..."
...In order to handle transformations more complex than translations and rotations, scale selection methods using the Laplacian/Difference of Gaussian operator (L/DoG) were introduced [19,23], and further extended with affine adaptation [2,24] to handle full affine transformations....
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...Covariant detectors differ by the type of features that they extract: points [11,16,36,6], circles [17,19,23], or ellipses [18,42,2,35,22,24]....
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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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1,574Ā 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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