Object recognition from local scale-invariant features
Citations
312Â citations
Cites background or methods from "Object recognition from local scale..."
...Of these, the two most notable and promising for dealing with captchas are SIFT [23] and SURF [1]....
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...would be to use very high level and complex image descriptors, such as SURF [1] and SIFT [23], that are invariant to rotation and very stable against distortion....
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...Of these, the two most notable and promising for dealing with captchas are SIFT [23] and SURF [1]....
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...When the captchas can t be segmented and we have to recognize the letters without segmentation, an alternative promising approach. would be to use very high level and complex image descriptors, such as SURF [1] and SIFT [23], that are invariant to rotation and very stable against distortion....
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311Â citations
Cites methods from "Object recognition from local scale..."
...The image features are detected using the Hessian-Laplace [17] operator, and described by a rotation variant SIFT descriptor [15]....
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311Â citations
Cites background from "Object recognition from local scale..."
...These variations are hard to be tackled by hand-crafted features such as SIFT [21], HOG [8] and color [30]....
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309Â citations
309Â citations
Cites background from "Object recognition from local scale..."
...Histogram of oriented gradients (HOG) by [11] and other 2D feature point descriptors, such as scale invariant feature transformation (SIFT) [44] are frequently encountered in ASLR approaches [8, 53]....
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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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1,574Â 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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