Object recognition from local scale-invariant features
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306 citations
Cites background from "Object recognition from local scale..."
...axes the similarity denition. Pooling is an important operation in Computer Vision, with a strong theoretical motivation. In the past, pooling has been introduced to obtain invariant representations [26,21]. Here, the justication is similar, but the goal is dierent: as we will see, the pooled indexes are aggregated in the proposed loss, allowing plasticity. Instead of the model acquiring invariance to...
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305 citations
Cites methods from "Object recognition from local scale..."
...For example, in computer vision, an early concept was Scale-Invariant Feature Transform (SIFT) [158] and histograms of oriented gradients (HoG) [159]....
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304 citations
Cites background or methods from "Object recognition from local scale..."
...Until recently, the majority of object recognition algorithms have depended upon some form of training phase [9, 18]....
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...By using descriptors that are invariant not just to translation, but also to rotation [16], scale [9] and affine warping [3, 12, 11], invariant features provide much more robust matching than previous correlation based methods....
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302 citations
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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