Object recognition from local scale-invariant features
Citations
104 citations
104 citations
Cites background or methods from "Object recognition from local scale..."
...These can be individual words of text, image features or any other simple descriptive features [see e.g. (Csurka et al., 2004; Joachims, 1998; Lowe, 1999)]....
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...In this study, we develop an approach based on the well-studied bag-of-words (BOW) (Csurka et al., 2004; Joachims, 1998; Lowe, 1999) algorithm to categorize and classify sets of TcR sequences from immunized and unimmunized mice at different times postimmunization....
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103 citations
Cites methods from "Object recognition from local scale..."
...An example is the scale-invariant feature tracker (SIFT) method developed by Lowe [33]....
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...The method presented by Lowe 88 builds a histogram of gradient orientations around the feature, then selects a primary orientation of gradient vector which corresponds to the maximum histogram bin....
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...For this work, this is accomplished using a variant of the scale-invariant feature tracking (SIFT) algorithm developed by Lowe [33]....
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...An example is the scale-invariant feature tracker (SIFT) method developed by Lowe [33]....
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...For more information of the SIFT feature transformation algorithm, see [33], [34] and [27]....
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103 citations
Cites methods from "Object recognition from local scale..."
...To estimate this transformation, Scale Invariant Feature Transform (SIFT) keypoints are extracted from the two frames [18] and a first, tentative nearest-neighbor matching is performed....
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103 citations
Cites methods from "Object recognition from local scale..."
...Since, in our case, the number of possible pose candidate can be OðQ2Þ for each pair of scene features, we employ importance sampling of matches based on a similarity measure in feature space and we progressively enforce geometric constraints between the descriptors base points in order to retain…...
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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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