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Methylomes Reveal Recent Evolutionary Changes in Populations of Two Plant Species. [PDF]

Genome Biol Evol
Korfmann K, Zauchner A, Huo B, Grünke C, Wang Y, Tellier A, Arunkumar R.   +6 more
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TOP-SIFT: the selected SIFT descriptor based on dictionary learning

The Visual Computer, 2018
The large amount of SIFT descriptors in an image and the high dimensionality of SIFT descriptor have made problems for the large-scale image database in terms of speed and scalability. In this paper, we present a descriptor selection algorithm based on dictionary learning to remove the redundant features and reserve only a small set of features, which ...
Deng Yu, Yujie Liu, Zongmin Li, Xiaoming Chen, Jianping Fan   +4 more
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Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis

Machine Vision and Applications, 2017
This paper aims to explore frequency behavior of isotropic (regular SIFT) and anisotropic (Bi-SIFT and Tri-SIFT) versions of the scale-space keypoint detection algorithm SIFT. We introduced a new smoothing function Trilateral filter that can be used in formation of a scale-space as an alternative to the Gaussian scale-space.
Ömer Soysal, Kazim Sekeroglu
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HALF-SIFT: High-Accurate Localized Features for SIFT

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
In this paper, the accuracy of feature points in images detected by the scale invariant feature transform (SIFT) is analyzed. It is shown that there is a systematic error in the feature point localization. The systematic error is caused by the improper subpel and subscale estimation, an interpolation with a parabolic function.
Bodo Rosenhahn, Jörn Ostermann, Kai Cordes, Oliver Müller   +3 more
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SIFTing Through Scales

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: matching a sparse set of scale invariant features, or dense matching using arbitrary scales.
Tal Hassner, Shay Filosof, Viki Mayzels, Lihi Zelnik-Manor   +3 more
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Secure outsourcing SIFT: Efficient and Privacy-Preserving Image Feature Extraction in the Encrypted Domain

IEEE Transactions on Dependable and Secure Computing, 2020
Multimedia data needs huge storage space, and application of multimedia data needs powerful capability of computing. Cloud computing can help owner of multimedia data to deal with it.
Linzhi Jiang, Chunxiang Xu, Xiaofang Wang, Bo Luo, Huaqun Wang   +4 more
semanticscholar   +1 more source

OS-SIFT: A Robust SIFT-Like Algorithm for High-Resolution Optical-to-SAR Image Registration in Suburban Areas

IEEE Transactions on Geoscience and Remote Sensing, 2018
Although the scale-invariant feature transform (SIFT) algorithm has been successfully applied to both optical image registration and synthetic aperture radar (SAR) image registration, SIFT-like algorithms have failed to register high-resolution (HR ...
Yuming Xiang, Feng Wang, H. You
semanticscholar   +1 more source

On SIFTs and their scales

2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012
Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: matching a sparse set of scale invariant features, or dense matching using arbitrary scales.
Lihi Zelnik-Manor, Viki Mayzels, Tal Hassner   +2 more
openaire   +2 more sources