Object recognition from local scale-invariant features
Citations
2,084 citations
Cites background from "Object recognition from local scale..."
...These approaches are particularly motivated by the success of the object recognition methodologies using sparse local appearance features, such as SIFT descriptors [Lowe 1999]....
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2,034 citations
Cites background or methods from "Object recognition from local scale..."
...The TextonBoost algorithm [1] was used with minor modifications reflecting the considerably different problem being posed in the VOC2006 as compared with the original work....
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...Inspired by the success of histogram-based descriptors for recognition [1,5,7,8], we use histograms of gradient orientation as image features....
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...[1] Chih-Chung Chang and Chih-Jen Lin....
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...The method used follows the method described in [1]....
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...Classification We used the implementation of [1] to train linear SVM classifiers on the normalized image histograms....
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2,000 citations
Cites background from "Object recognition from local scale..."
...Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid....
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1,897 citations
Cites background from "Object recognition from local scale..."
..., SIFT [178] and HOG [52]) and to explore approaches (e....
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...Handcrafted local invariant features gained tremendous popularity, starting from the Scale Invariant Feature Transform (SIFT) feature [178], and the progress on various visual recognition tasks was based substantially on the use of local descriptors [187] such as Haar-like features [276], SIFT [179], Shape Contexts [12], Histogram of Gradients (HOG) [52] Local Binary Patterns (LBP) [196], and region covariances [268]....
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...4 Milestones of object detection and recognition, including feature representations [47, 52, 101, 140, 147, 178, 179, 212, 248, 252, 263, 276, 279], detection frameworks [74, 85, 239, 271, 276], and datasets [68, 166, 234]....
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1,896 citations
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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