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Local Invariant Feature Detectors: A Survey
Tinne Tuytelaars,Krystian Mikolajczyk +1 more
- 16 Jun 2008
TL;DR: An overview of invariant interest point detectors can be found in this paper, where an overview of the literature over the past four decades organized in different categories of feature extraction methods is presented.
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Abstract: In this survey, we give an overview of invariant interest point detectors, how they evolvd over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.
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Citations
Structure-from-Motion Revisited
Johannes L. Schonberger,Jan-Michael Frahm +1 more
- 27 Jun 2016
TL;DR: This work proposes a new SfM technique that improves upon the state of the art to make a further step towards building a truly general-purpose pipeline.
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
Relja Arandjelovic,Petr Gronat,Akihiko Torii,Tomas Pajdla,Josef Sivic +4 more
- 26 Jun 2016
TL;DR: A convolutional neural network architecture that is trainable in an end-to-end manner directly for the place recognition task and an efficient training procedure which can be applied on very large-scale weakly labelled tasks are developed.
FREAK: Fast Retina Keypoint
Alexandre Alahi,Raphael Ortiz,Pierre Vandergheynst +2 more
- 16 Jun 2012
TL;DR: This work proposes a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK), which is in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK.
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
TL;DR: A convolutional neural network architecture that is trainable in an end-to-end manner directly for the place recognition task, and significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks.
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•Posted Content
NetVLAD: CNN architecture for weakly supervised place recognition
TL;DR: NetVLAD as discussed by the authors is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval, which is readily pluggable into any CNN architecture and amenable to training via backpropagation.
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References
Patch-based stereo in a general binocular viewing geometry
Boaz J. Super,W.N. Klarquist +1 more
TL;DR: A one-stage stereo algorithm that yields 3D planar surface patches directly from matching image patch intensity information and quantitatively evaluated against ground truth on real images with difficult viewing geometries is presented.
19
Corner detectors for affine invariant salient regions: is color important?
Nicu Sebe,Theo Gevers,Joost van de Weijer,Sietse Dijkstra +3 more
- 13 Jul 2006
TL;DR: This work shows that color information can make a significant contribution to feature detection and matching and suggests that to obtain optimal performance, a tradeoff should be made between invariance and distinctiveness by an appropriate weighting of the intensity and color information.
•Proceedings Article
A bottom-up attention system for active vision
Ruggero Milanese,Jean Marc Bost,Thierry Pun +2 more
- 30 Aug 1992
18
Finding "Vertices" in a picture"
H. Y. Fend,Theodosios Pavlidis +1 more
TL;DR: This paper describes three algorithms for determining points which are common to the boundaries of three regions in terms of discrete representations of the boundaries.
17
Digital Image Processing and Recognition
TL;DR: This paper reviews some of the recent developments in image recognition techniques, including data structures for image analysis; image matching; segmentation; texture analysis; and shape description.
16
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