Distinctive Image Features from Scale-Invariant Keypoints
Citations
82 citations
82 citations
Cites background from "Distinctive Image Features from Sca..."
...All recent approaches in image retrieval and classification use a set of local descriptors to encode the information contained in the image, such as SIFT [1]....
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...However clustering high dimensional feature spaces (typically 128 dimensions for SIFT) into that many clusters is not an easy task....
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...We extracted SIFT features on a dense grid with 3 different scales in order to obtain bags of about 15000 SIFT for each image (about 150 millions of descriptors for the whole dataset)....
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...The first step is the introduction of powerful descriptors with high discriminative capacities like SIFT [1]....
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...IMAGE REPRESENTATION AND SIMILARITIES All recent approaches in image retrieval and classification use a set of local descriptors to encode the information contained in the image, such as SIFT [1]....
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82 citations
82 citations
Cites background or methods or result from "Distinctive Image Features from Sca..."
...The best-known and widely used feature detector and descriptor scheme was introduced by Lowe [13] and is called SIFT (Scale-invariant feature transform)....
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...As in [13], the scalespace is divided in octaves (i....
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...Detection of the interest points are then performed using the description method from SIFT [13]: we implement SIFT descriptor, tuning the parameters of the algorithm to improve reliability....
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...According to our experience, the determinant of the Hessian seems to be more stable with motion blurred images than Difference-of-Gaussian (DoG) used in [13]....
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...Indeed, the classical feature detectors and descriptors [13], [1] that proved to work well for wheeled robots, do not perform reliably in presence of motion blur....
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82 citations
Additional excerpts
...To this end, we explore the seminal image/feature pyramid concept [1,21,31,8]....
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References
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