Results 11 to 20 of about 391,798 (266)
Sparse Convolution for Approximate Sparse Instance
CoRR, 2023 Computing the convolution $A \star B$ of two vectors of dimension $n$ is one of the most important computational primitives in many fields. For the non-negative convolution scenario, the classical solution is to leverage the Fast Fourier Transform whose time complexity is $O(n \log n)$.
Xiaoxiao Li +2 more
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Frontiers in Human Neuroscience, 2014 Entrained behavior coordinates, predicts, and modulates multi-scale rhythmic gestures with high spatio-temporal precision even as it shows flexible adaptation in response to perturbation (Clayton et al., 2005; Altenmuller et al., 2006; Phillips-Silver et al., 2010).
Eric eBarnhill, Eric eBarnhill
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Deterministic sparse FFT for M-sparse vectors [PDF]
Numerical Algorithms, 2017 In this interesting paper, the authors present a new deterministic sparse (inverse) fast Fourier transform (FFT), where the resulting vector \(\mathbf x \in {\mathbb C}^N\) with \(N = 2^J\) is \(M\)-sparse, i.e., \(\mathbf x\) contains only \(M\) nonzero components. This new algorithm which generalizes the sparse FFT of \textit{G. Plonka} and \textit{K.
Gerlind Plonka +3 more
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Sparse Sparse Bundle Adjustment [PDF]
Procedings of the British Machine Vision Conference 2010, 2010 Sparse Bundle Adjustment (SBA) is a method for simultaneously optimizing a set of camera poses and visible points. It exploits the sparse primary structure of the problem, where connections exist just between points and cameras. In this paper, we implement an efficient version of SBA for systems where the secondary structure (relations among cameras ...
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Computational Linguistics, 2021 AbstractThe transcription bottleneck is often cited as a major obstacle for efforts to document the world’s endangered languages and supply them with language technologies. One solution is to extend methods from automatic speech recognition and machine translation, and recruit linguists to provide narrow phonetic transcriptions and sentence-aligned ...
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Efficient Tuning-Free l1-Regression of Nonnegative Compressible Signals
Frontiers in Applied Mathematics and Statistics, 2021 In compressed sensing the goal is to recover a signal from as few as possible noisy, linear measurements with the general assumption that the signal has only a few non-zero entries.
Hendrik Bernd Petersen +3 more
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Robust Recovery of Corrupted Image Data Based on $L_{1-2}$ Metric
IEEE Access, 2018 For removing noises and recovering intrinsic structure from corrupted image data, a classic modeling approach is based on sparsity assumption. In traditionally, the sparsity is measured by L1-norm.
Fanlong Zhang +3 more
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2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2020 MP4 is currently the gold standard for video compression. Here we demonstrate that MP4 can be augmented by using a spatiotemporal sparse code optimized for the reconstruction of video portraits to up-sample data streams in which 75% of the pixels have been removed.
Daniel A. Wang +5 more
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Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm
IEEE Access, 2019 Hyperspectral anomaly detection is a research hot spot in the field of remote sensing. It can distinguish abnormal targets from the scene just by utilizing the spectral differences and requiring no prior information.
Yuan Yuan, Dandan Ma, Qi Wang
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Algorithms, 2018 Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level.
Wei Xu +4 more
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