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Scale-invariant feature transform

Known as: Autopano-sift, Scale invariant feature transform, Autopano Pro 
Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was… Expand
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Papers overview

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Review
2017
Review
2017
Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability… Expand
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2016
2016
[These lecture notes complement the slides. You should read both!] Suppose that we have computed a scale space using a ∇ 2 g σ (x… Expand
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Highly Cited
2012
Highly Cited
2012
Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching developed by David Lowe (1999,2004… Expand
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Highly Cited
2011
Highly Cited
2011
Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of… Expand
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Highly Cited
2011
Highly Cited
2011
A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local… Expand
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Highly Cited
2009
Highly Cited
2009
If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo… Expand
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Highly Cited
2008
Highly Cited
2008
Over the years, several spatio-temporal interest point detectors have been proposed. While some detectors can only extract a… Expand
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Highly Cited
2004
Highly Cited
2004
This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable… Expand
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Highly Cited
2003
Highly Cited
2003
We introduce a novel method for constructing and selecting scale-invariant object parts. Scale-invariant local descriptors are… Expand
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Highly Cited
1999
Highly Cited
1999
  • David G. Lowe
  • Proceedings of the Seventh IEEE International…
  • 1999
  • Corpus ID: 5258236
An object recognition system has been developed that uses a new class of local image features. The features are invariant to… Expand
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