On SIFTs and their scales

@article{Hassner2012OnSA,
  title={On SIFTs and their scales},
  author={Tal Hassner and Viki Mayzels and Lihi Zelnik-Manor},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={1522-1528}
}
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. In this paper we turn our attention to the overwhelming majority of pixels, those where stable scales are not found by standard techniques. We ask, is scale-selection necessary for these pixels, when dense, scale-invariant matching… Expand
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References

SHOWING 1-10 OF 37 REFERENCES
Scale invariance without scale selection
  • I. Kokkinos, A. Yuille
  • Computer Science, Mathematics
  • 2008 IEEE Conference on Computer Vision and Pattern Recognition
  • 2008
TLDR
This work constructs scale invariant descriptors (SIDs) without requiring the estimation of image scale and shows that the constructed SIDs outperform state-of-the-art descriptors on standard datasets and the performance of a boundary-based model is systematically improved on an object detection task. Expand
Is SIFT scale invariant
This note is devoted to a mathematical exploration of whether Lowe's Scale-Invariant Feature Transform (SIFT)[21], a very successful image matching method, is similarity invariant as claimed. It isExpand
The Generalized PatchMatch Correspondence Algorithm
TLDR
This paper generalizes PatchMatch in three ways: to find k nearest neighbors, as opposed to just one, to search across scales and rotations, in addition to just translations, and to match using arbitrary descriptors and distances, not just sum-of-squared-differences on patch colors. Expand
Speeded-Up Robust Features (SURF)
TLDR
A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. Expand
A Performance Evaluation of Local Descriptors
TLDR
It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors. Expand
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
TLDR
An EM-based algorithm to compute dense depth and occlusion maps from wide-baseline image pairs using a local image descriptor, DAISY, which is very efficient to compute densely and robust against many photometric and geometric transformations. Expand
Scale & Affine Invariant Interest Point Detectors
TLDR
A comparative evaluation of different detectors is presented and it is shown that the proposed approach for detecting interest points invariant to scale and affine transformations provides better results than existing methods. Expand
SIFT Flow: Dense Correspondence across Different Scenes
TLDR
A method to align an image to its neighbors in a large image collection consisting of a variety of scenes, and applies the SIFT flow algorithm to two applications: motion field prediction from a single static image and motion synthesis via transfer of moving objects. Expand
Sampling Strategies for Bag-of-Features Image Classification
TLDR
It is shown experimentally that for a representative selection of commonly used test databases and for moderate to large numbers of samples, random sampling gives equal or better classifiers than the sophisticated multiscale interest operators that are in common use. Expand
Feature Detection with Automatic Scale Selection
  • T. Lindeberg
  • Computer Science
  • International Journal of Computer Vision
  • 2004
TLDR
It is shown how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation and how it can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure. Expand
...
1
2
3
4
...