Distinctive Image Features from Scale-Invariant Keypoints
Citations
156 citations
155 citations
Cites methods from "Distinctive Image Features from Sca..."
...The patches are computed by sparse key-point detection algorithms like SIFT (Lowe 2004), FAST (Agrawal et al....
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...When a new observation comes, the visual features, such as SIFT (Lowe 2004) or SURF (Bay et al....
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...The patches are computed by sparse key-point detection algorithms like SIFT (Lowe 2004), FAST (Agrawal et al. 2008) or ORB (Rublee et al. 2011) and are then filtered to spread over the whole image....
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...When a new observation comes, the visual features, such as SIFT (Lowe 2004) or SURF (Bay et al. 2006), are extracted and the descriptors are approximated by the entries in the dictionary....
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154 citations
Cites background from "Distinctive Image Features from Sca..."
...One is the bag of visual words (BOVWs) model [12]–[14], which generally includes three steps: 1) extracting man-made visual features, such as scale invariant feature transform (SIFT) [15] and histogram of oriented gradient [16] descriptors; 2) clustering features to form visual words (clustering centers) by using k-means or other clustering methods; and 3) mapping visual features to the closest word and generating a mid-level feature representation by word histograms....
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...generally includes three steps: 1) extracting man-made visual features, such as scale invariant feature transform (SIFT) [15]...
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...Sheng et al. [10] proposed to use SC to generate three mid-level representations based on SIFT, local ternary pattern histogram Fourier, and color histogram features, respectively....
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154 citations
Cites methods from "Distinctive Image Features from Sca..."
...The characteristics of HOG is no invariant for scale and rotation, which is different from the standard local descriptors such as SIFT [Lowe(2004)] and SURF [Bay et al.(2008)]....
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153 citations
Cites background or methods from "Distinctive Image Features from Sca..."
...In 2D images, the concept of scale space is often described with a family of gradually smoothed images created through the convolution of a Gaussian kernel, such a Gaussian scale-space has a wide range of applications in image processing and 2D computer vision, such as in edge sharpening and interest point selection [12]....
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...The DoG is an approximation to the Laplacian of the Gaussian (LoG) operator, and is widely used in applications such as image enhancement, blob detection, edge detection, finding points of salience, presegmenting images [13], and perhaps most notably in the form of a DoG pyramid for obtaining scale invariance in 2D object recognition [12]....
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References
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