Results 11 to 20 of about 522,381 (263)
Sparse Representation-based Open Set Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification.
Patel, Vishal M., Zhang, He
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Sparse linear representation [PDF]
2009 IEEE International Symposium on Information Theory, 2009 5 pages, to appear in proc.
Jeong, Halyun, Kim, Young-Han
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Sparse Representation Based Projections [PDF]
Procedings of the British Machine Vision Conference 2011, 2011 In dimensionality reduction most methods aim at preserving one or a few properties of the original space in the resulting embedding. As our results show, preserving the sparse representation of the signals from the original space in the (lower) dimensional projected space is beneficial for several benchmarks (faces, traffic signs, and handwritten ...
Timofte, Radu, Van Gool, Luc
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Sparse time-frequency representations [PDF]
Proceedings of the National Academy of Sciences, 2006 Auditory neurons preserve exquisite temporal information about sound features, but we do not know how the brain uses this information to process the rapidly changing sounds of the natural world. Simple arguments for effective use of temporal information led us to consider the reassignment class of time-frequency representations as ...
Gardner, Timothy J. +1 more
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3-D Sparse Representations [PDF]
, 2014 In this chapter we review a variety of 3D sparse representations developed in recent years and adapted to different kinds of 3D signals. In particular, we describe 3D wavelets, ridgelets, beamlets and curvelets. We also present very recent 3D sparse representations on the 3D ball adapted to 3D signal naturally observed in spherical coordinates ...
Lanusse, François +3 more
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Sparse representation of astronomical images [PDF]
Journal of the Optical Society of America A, 2013 Sparse representation of astronomical images is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm: i)Effectiveness at producing sparse representations.
Rebollo-Neira, Laura, Bowley, James
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Sparse signal representation for complex-valued imaging [PDF]
, 2009 We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature.
Cetin, Mujdat +3 more
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Joint Sparse Representation-based Single Image Super-Resolution for Remote Sensing Applications
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Sparse representation-based single image super-resolution (SISR) methods use a coupled overcomplete dictionary trained from high-resolution images/image patches.
Bhabesh Deka +3 more
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Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling [PDF]
, 2014 We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code representation of an image.
Maire, Michael +2 more
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Deep Sparse Representation-Based Classification [PDF]
IEEE Signal Processing Letters, 2019 We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. The role of the autoencoder network is to learn robust deep features for classification. On the other hand, the fully-connected layer,
Mahdi Abavisani, Vishal M. Patel
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