Learning Separable Filters

Keywords

Separable space, Computer science, Filter (signal processing), Computational complexity theory, Task (project management)

References

• Locating blood vessels in retinal images by piecewise threshold probing of a matched filter responseAdam Hoover, Valentina L. Kouznetsova, Michael H. Goldbaum — 2000
• Representing and Recognizing the Visual Appearance of Materials using Three-dimensional TextonsThomas Leung, Jitendra Malik — 2001
• Pattern Recognition and Machine LearningChris Bishop — 2006
• Filter Learning for Linear Structure SegmentationRoberto Rigamonti, Engin Türetken, Germán González — 2011
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• Hardware accelerated convolutional neural networks for synthetic vision systemsClément Farabet, Berin Martini, Polina Akselrod — 2010
• Separable Dictionary LearningSimon Hawe, Matthias Seibert, Martin Kleinsteuber — 2013
• The Design and Use of Steerable FiltersWilliam T. Freeman, Edward H. Adelson — 1991
• FOUNDATION OF EVALUATIONC. J. van Rijsbergen — 1974
• Constant‐Time Filtering by Singular Value Decomposition †Craig Gotsman — 1994
• Tensor Decompositions and ApplicationsTamara G. Kolda, Brett W. Bader — 2009
• Steerable part modelsHamed Pirsiavash, Deva Ramanan — 2012
• Handwritten digit classification using higher order singular value decompositionBerkant Savas, Lars Eldén — 2007
• Sparse Representation for Computer Vision and Pattern RecognitionJohn Wright, Yi Ma, Julien Mairal — 2010
• A scalable optimization approach for fitting canonical tensor decompositionsEvrim Acar, Daniel Dunlavy, Tamara G. Kolda — 2011
• Learning Deep Architectures for AIYoshua Bengio — 2007
• Discriminative Learning of Local Image DescriptorsMatthew A. Brown, Gang Hua, Simon Winder — 2011
• Learning mid-level features for recognitionY-Lan Boureau, Francis Bach, Yann LeCun — 2010
• Optimization with Sparsity-Inducing PenaltiesFrancis Bach — 2011
• Double Sparsity: Learning Sparse Dictionaries for Sparse Signal ApproximationRon Rubinstein, Michael Zibulevsky, Michael Elad — 2010

Cited by

• Learning multi-channel correlation filter bank for eye localizationShiming Ge, Rui Yang, Yuqing He — 2016
• Supervised Feature Learning for Curvilinear Structure SegmentationCarlos Becker, Roberto Rigamonti, Vincent Lepetit — 2013
• TILDE: A Temporally Invariant Learned DEtectorYannick Verdié, Kwang Moo Yi, Pascal Fua — 2014
• Efficient Dictionary Learning with Sparseness-Enforcing ProjectionsMarkus Thom, Matthias Rapp, Günther Palm — 2015
• Speeding up Convolutional Neural Networks with Low Rank ExpansionsMax Jaderberg, Andrea Vedaldi, Andrew Zisserman — 2014
• Toward Fast Transform LearningOlivier Chabiron, François Malgouyres, Jean‐Yves Tourneret — 2014
• Learning Separable FiltersAmos Sironi, Bugra Tekin, Roberto Rigamonti — 2015
• Multiscale Centerline Detection by Learning a Scale-Space Distance TransformAmos Sironi, Vincent Lepetit, Pascal Fua — 2014
• The Fastest Deformable Part Model for Object DetectionJunjie Yan, Zhen Lei, Longyin Wen — 2014
• Learning Separable Filters with Shared PartsBugra Tekin — 2013
• Accelerating Very Deep Convolutional Networks for Classification and DetectionXiangyu Zhang, Jianhua Zou, Kaiming He — 2015
• Sparse 3D convolutional neural networksBen Graham — 2015
• Speeding-up Convolutional Neural Networks Using Fine-tuned CP-DecompositionVadim Lebedev, Yaroslav Ganin, Maksim Rakhuba — 2014
• Sparse Space-Time Deconvolution for Calcium Image AnalysisFerrán Diego Andilla, Fred A. Hamprecht — 2014
• Predicting Parameters in Deep LearningShakibi, Babak — 2013
• Training CNNs with Low-Rank Filters for Efficient Image ClassificationYani Ioannou, Duncan Robertson, Jamie Shotton — 2015
• Convolutional neural networks with low-rank regularizationCheng Tai, Tong Xiao, Yi Zhang — 2015
• Deep learning for text spottingAndrea Vedaldi — 2015
• Exploiting Local Structures with the Kronecker Layer in Convolutional NetworksShuchang Zhou, Jianan Wu, Yuxin Wu — 2015
• Multiple-Hypothesis Affine Region Estimation with Anisotropic LoG FiltersTakahiro Hasegawa, Mitsuru Ambai, Kohta Ishikawa — 2015

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