Object recognition from local scale-invariant features
Citations
190 citations
Cites methods from "Object recognition from local scale..."
...All metrics are based on the χ2 distance for histograms of quantized i) SIFT [19] ii) colour and ii) texture features integrated over the superpixel....
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...We can combine arbitrary heterogeneous features such as colour, texture and SIFT histograms, superpixel area and location, GIST, and so on....
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...All metrics are based on the χ(2) distance for histograms of quantized i) SIFT [19] ii) color and ii) texture features integrated over the superpixel....
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...In [8] multiple visual cues (SIFT, colour and position cues) were used, but only for unary potentials....
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...We present experiments on the LabelMe subset introduced in [10] (called the SIFT-flow data-set)....
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189 citations
189 citations
189 citations
Cites methods from "Object recognition from local scale..."
...The gradient features are extracted from the edge map [4,3] or oriented gradients, which mainly include SIFT [8], EOH [7], HOG [1], covariance matrix[17], shapelet [15], and edgelet [19]....
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189 citations
Cites methods from "Object recognition from local scale..."
...Deep learning has transformed the traditional way of manually designing features [13, 14] into automatic extraction [4, 29, 30]....
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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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