Distinctive Image Features from Scale-Invariant Keypoints
Citations
77 citations
Cites methods from "Distinctive Image Features from Sca..."
...They are SIFT [29] features extracted on 9 face key-points (eye corners, nose corners, nose tip and mouth corners) at 3 different scales....
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77 citations
Cites methods from "Distinctive Image Features from Sca..."
...Remove images without matched image from ; the image with most matched images in ; Update: while is not null the image with most matched images in ; Number of SIFT features in ; for Count the number of matched SIFT point between image and image ; end if then image can be viewed as near duplicate with image update: otherwise image is assigned as a representative image for the centroid, update: end Output: representative images for the GPS location refined centroid...
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...3) Scale Invariant Feature Transform (SIFT): The images could be further described via the local interest point descriptors given by SIFT [5]....
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...There are too many SIFT points in the background, and only a few on themountain, so local feature refinement cannot improve the GPS estimation performance....
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...If two images have sufficient matched SIFT point pairs[7], [36], they are considered a match....
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...Then for the offline dataset, each SIFT point is quantized into one of the centroids....
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77 citations
Cites background from "Distinctive Image Features from Sca..."
...In [26], the descriptor is rotated after its computation to fit that main orientation....
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...The most prominent and widely successful one is the SIFT feature detector [26]....
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77 citations
Cites background from "Distinctive Image Features from Sca..."
...The most famous among them is SIFT (Scale Invariant Feature Transform) [68], where at first salient points of the image are chosen via in interest operator that looks for “stable” locations in...
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...The most famous among them is SIFT (Scale Invariant Feature Transform) [68], where at first salient points of the image are chosen via in interest operator that looks for “stable” locations in the image (i.e. locations that are identifiable over different scales and rotations)....
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77 citations
Cites background from "Distinctive Image Features from Sca..."
...The most popular local patch descriptor is SIFT in the computer vision community [23]....
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References
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