Object recognition from local scale-invariant features
Citations
99 citations
Cites methods from "Object recognition from local scale..."
...The feature vectors used in our experiments include MLBP, DSIFT, and the concatenation of MLBP and DSIFT extracted from three different regions....
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...In most FR literature, the MLBP and SIFT features are usually extracted from the grayscale (intensity) images....
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...A concatenation of the MLBP and DSIFT features extracted from the intensity face image was used....
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...To show the robustness of the proposed approach against different texture descriptors, we also used densely sampled SIFT (DSIFT) features in our experiments....
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...This inspired us to capture moiré patterns using a number of well known texture descriptors, such as MLBP [12] and SIFT [10] to use for spoof detection....
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99 citations
98 citations
Cites methods from "Object recognition from local scale..."
...Our result even holds when the features are fixed, as when using tailored representations such as SIFT [35]....
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98 citations
Cites background from "Object recognition from local scale..."
...Another line of research focuses on the robust feature representation of images, such as the VLAD [9], Fisher vector [10] with SIFT features [11]....
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98 citations
Cites background or methods from "Object recognition from local scale..."
...A lot of work has been done for angle/similarity recognition using keypoint-based local descriptors like SIFT [12], however, this kind of tools works only on textured objects and fails to describe smooth shapes or drawings (i....
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...• For the geometric hashing, we need feature points, hence we have extracted Harris [20] and DoG keypoints [21], [12] in each pattern....
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...A lot of work has been done for angle/similarity recognition using keypoint-based local descriptors like SIFT [12], however, this kind of tools works only on textured objects and fails to describe smooth shapes or drawings (i.e. sketch) for instance....
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...The computation of Zernike moments is made faster by precomputing a set of 100×100 Zernike filters and by applying a fast smoothing approach along scales (like pyramids of Gaussian in [12]) to quickly sample each 100×100 window....
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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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