Results 161 to 170 of about 1,177,560 (190)

3D Reconstruction with Sparse Image Sets

2011 Irish Machine Vision and Image Processing Conference, 2011
3D reconstruction with sparse image sets requires more accurate view geometry estimation than a large number of images based 3D reconstruction. In this paper, we have proposed an automatic 3D reconstruction system based on a small set of images which can estimate the view transformation between different views accurately.
Jiao Tian, Derek Molloy
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Speech Reconstruction by Sparse Linear Prediction

2012
This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show that the use of a single high-order linear predictor can provide better results than the classic LSAR ...
Ján Koloda, Antonio M. Peinado, Victoria E. Sánchez   +2 more
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Bayesian learning for sparse signal reconstruction

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
Sparse Bayesian learning and specifically relevance vector machines have received much attention as a means of achieving parsimonious representations of signals in the context of regression and classification. We provide a simplified derivation of this paradigm from a Bayesian evidence perspective and apply it to the problem of basis selection from ...
David P. Wipf, Bhaskar D. Rao
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Learning Splines for Sparse Tomographic Reconstruction

2014
In a few-view or limited-angle computed tomography (CT), where the number of measurements is far fewer than image unknowns, the reconstruction task is an ill-posed problem. We present a spline-based sparse tomographic reconstruction algorithm where content-adaptive patch sparsity is integrated into the reconstruction process.
Elham Sakhaee, Alireza Entezari
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Evolutionary algorithms for sparse signal reconstruction

Signal, Image and Video Processing, 2019
This study includes an evolutionary algorithm technique for sparse signal reconstruction in compressive sensing. In general, l1 minimization and greedy algorithms are used to reconstruct sparse signals. In addition to these methods, recently, heuristic algorithms have begun to be used to reconstruct sparse signals.
Murat Emre Erkoc, Nurhan Karaboga
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DOA Estimation for Sparse Array via Sparse Signal Reconstruction

IEEE Transactions on Aerospace and Electronic Systems, 2013
The problem of direction-of-arrival (DOA) estimation for sparse array is addressed. The perspective that DOA estimation in virtual array response model can be cast as the problem of sparse recovery is introduced. Two methods are proposed, based on different optimization problems, which are solvable using second-order cone (SOC) programming. Without the
Nan Hu 0001, Zhongfu Ye, Xu Xu 0003, Ming Bao   +3 more
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Sparse modelling and sparse signal reconstruction

Abstract Signal models are central to solving inverse problems, and reconstruction methods either implicitly or explicitly make use of signal models. Assuming the unknown signal of interest lies in a class of signals described by a signal model, we wish to reconstruct the signal with an algorithm that is sample efficient (i.e., only ...
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Reconstructing sparse trigonometric functions

ACM Communications in Computer Algebra, 2011
Cuyt, Annie, Lee, Wen-Shin
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Fourier diffusion for sparse CT reconstruction

Medical Imaging 2024: Physics of Medical Imaging
Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing.
Anqi, Liu, Grace J, Gang, J Webster, Stayman   +2 more
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Sparse-Prony FRI signal reconstruction

Signal, Image and Video Processing, 2023
P. Sudhakar Reddy, B. S. Raghavendra, A. V. Narasimhadhan   +2 more
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