Results 41 to 50 of about 796,535 (249)
Sparse codes as Alpha Matte [PDF]
Proceedings of the British Machine Vision Conference 2014, 2014 In this paper, image matting is cast as a sparse coding problem wherein the sparse codes directly give the estimate of the alpha matte. Hence, there is no need to use the matting equation that restricts the estimate of alpha from a single pair of foreground (F) and background (B) samples.
Johnson, Jubin +2 more
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Sparse-Coding Variational Autoencoders [PDF]
Neural Computation, 2018 Abstract The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations; however: (1) computing the neural response to an image patch required minimizing a nonlinear ...
Victor Geadah +4 more
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Image Classification Based on Neighborhood Preserving Embedding Sparse Coding [PDF]
Jisuanji gongcheng, 2016 Aiming at the problem of image classification with a complex background,this paper proposes a new image algorithm based on Neighborhood Preserving Embedding regularization Sparse Coding algorithm(NPESC).Comparing with traditional sparse coding,it adds ...
GAO Jiaxue,CHEN Xiuhong
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Voxel selection in fMRI data analysis based on sparse representation [PDF]
, 2009 Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI)
Namburi, Praneeth +5 more
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Transformational Sparse Coding
CoRR, 2017 A fundamental problem faced by object recognition systems is that objects and their features can appear in different locations, scales and orientations. Current deep learning methods attempt to achieve invariance to local translations via pooling, discarding the locations of features in the process.
Dimitrios C. Gklezakos, Rajesh P. N. Rao
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Efficient ab initio many-body calculations based on sparse modeling of Matsubara Green's function
SciPost Physics Lecture Notes, 2022 This lecture note reviews recently proposed sparse-modeling approaches for efficient ab initio many-body calculations based on the data compression of Green's functions.
Hiroshi Shinaoka, Naoya Chikano, Emanuel Gull, Jia Li, Takuya Nomoto, Junya Otsuki, Markus Wallerberger, Tianchun Wang, Kazuyoshi Yoshimi
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Class-specific sparse codes for representing activities [PDF]
, 2015 In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state ...
Denman, Simon +7 more
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CoRR, 2012 We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function.
Jun Zhu 0001, Eric P. Xing
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On Design and Efficient Decoding of Sparse Random Linear Network Codes
IEEE Access, 2017 While random linear network coding is known to improve network reliability and throughput, its high costs for delivering coding coefficients and decoding represent an obstacle where nodes have limited power to transmit and decode packets.
Ye Li, Wai-Yip Chan, Steven D. Blostein
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Nature Communications, 2023 Neural computation is often traced in terms of either rate- or phase-codes. However, most circuit operations will simultaneously affect information across both coding schemes. It remains unclear how phase and rate coded information is transmitted, in the
Daniel Müller-Komorowska +3 more
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