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We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture.
Rotationally invariant image operators
Paul Beaudet · 1978
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A combined corner and edge detector
Chris Harris and Mike Stephens · 1988
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Indexing based on scale invariant interest points
Krystian Mikolajczyk and Cordelia Schmid · 2001
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The gaussian scale-space paradigm and the multiscale local jet
Luc Florack, Bart Ter Haar Romeny, Max Viergever, and Jan Koenderink · 2002
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Object recognition using local affine frames on distinguished regions
Stepan Obdrzalek and Jiri Matas · 2002
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Distinctive image features from scale-invariant keypoints
David G. Lowe · 2004
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Scale & affine invariant interest point detectors
Krystian Mikolajczyk and Cordelia Schmid · 2004
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Robust wide-baseline stereo from maximally stable extremal regions
Jiri Matas, Chum Ondrej, Urban Martin, and Pajdla Tomás · 2004
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A performance evaluation of local descriptors
Krystian Mikolajczyk and Cordelia Schmid · 2005
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A comparison of affine region detectors
Krystian Mikolajczyk, Tinne Tuytelaars, Cordelia Schmid, Andrew Zisserman, Jiri Matas, Frederik Schaffalitzky, Timor Kadir, and Luc Van Gool · 2005
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Machine learning for high-speed corner detection
Edward Rosten and Tom Drummond · 2006
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Local invariant feature detectors: a survey
Tinne Tuytelaars and Krystian Mikolajczyk · 2008
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Speeded-up robust features (surf)
Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool · 2008
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Faster and better: A machine learning approach to corner detection
Edward Rosten, Reid Porter, and Tom Drummond · 2010
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Brisk: Binary robust invariant scalable keypoints
Stefan Leutenegger, Chli Margarita, and Siegwart Roland · 2011
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Orb: An efficient alternative to sift or surf
Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski · 2011
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Kaze features
Pablo Fernández Alcantarilla, Adrien Bartoli, and Andrew J. Davison · 2012
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Fast explicit diffusion for accelerated features in nonlinear scale spaces
Pablo Fernández Alcantarilla, Jesús Nuevo, and Adrien Bartoli · 2013
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Predicting matchability
Wilfried Hartmann, Michal Havlena, and Konrad Schindler · 2014
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Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2017
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Learning discriminative and transformation covariant local feature detectors
Xu Zhang, Felix X. Yu, Svebor Karaman, and Shih-Fu Chang · 2017
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Superpoint: Self-supervised interest point detection and description
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2017
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Quad-networks: unsupervised learning to rank for interest point detection
Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, and Marc Pollefeys · 2017
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Domain-size pooling in local descriptors: Dsp-sift
Jingming Dong and Stefano Soatto · 2017
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Learning to compare image patches via convolutional neural networks
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Learning covariant feature detectors
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Learning to assign orientations to feature points
Kwang Moo Yi, Yannick Verdie, Pascal Fua, and Vincent Lepetit · 2016
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Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas · 2017
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Large scale evaluation of local image feature detectors on homography datasets
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End-to-end learning of keypoint detector and descriptor for pose invariant 3d matching
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Learning to find good correspondences
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Repeatability is not enough: Learning affine regions via discriminability
Dmytro Mishkin, Filip Radenovic, and Jiri Matas · 2018
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Discovery of latent 3d keypoints via end-to-end geometric reasoning
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