Object recognition from local scale-invariant features
Citations
293 citations
Cites methods from "Object recognition from local scale..."
...Instead of pooling over hand-designed local descriptors, such as SIFT [31], one could learn a ConvNet end-to-end, with a structured layer of the form C = log(X⊤X + ǫI) (4) where ǫI is a regularizer preventing log singularities around 0 when the covariance matrix is not full rank....
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...O2P over hand-crafted SIFT performs considerably less well than our DeepO2P models, suggesting that large potential gains can be achieved when deep features replace existing descriptors....
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...For recognition, we illustrate deep, fully trainable architectures, with a type of pooling layer that proved dominant for free-form region description [6], at the time on top of standard manually designed local features such as SIFT....
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...Instead of pooling over hand-designed local descriptors, such as SIFT [31], one could learn a ConvNet end-to-end, with a structured layer of the form...
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293 citations
293 citations
Cites methods from "Object recognition from local scale..."
...The hand-on engineering methods extract features using traditional approaches such as HoG [11], SIFT [12], LBP [13], Gabor filters [14] and etc which fails to encode the variations in scale, rotation, and illumination....
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292 citations
Cites methods from "Object recognition from local scale..."
...The SIFT algorithm computes keypoints that are useful for matching....
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...Extraction and matching of features between the images is done using scale-invariant feature transform (SIFT) [39]....
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...Figure 12 shows the SIFT matches between two successive surface images....
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292 citations
Cites background or methods from "Object recognition from local scale..."
...The SIFT image representation (Lowe 1999), which predates HOG, is conceptually very similar to HOG with slight differences in the normalization and gradient sampling and pooling schemes....
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...One of the common approaches is to sample the nuisance feature space sparsely with various “detectors”—such as local feature detectors with different normalization schemes (Lindeberg 1998; Lowe 1999; Mikolajczyk and Schmid 2003), bounding box proposals (Uijlings et al....
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...…the nuisance feature space sparsely with various “detectors”—such as local feature detectors with different normalization schemes (Lindeberg 1998; Lowe 1999; Mikolajczyk and Schmid 2003), bounding box proposals (Uijlings et al. 2013; Zitnick and Dollar 2014) or a direct regression of the…...
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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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