Results 41 to 50 of about 114,572 (313)
Image fusion based on group sparse representation [PDF]
, 2016 Sparse representation based image fusion has been widely studied recently. However, it's not popular in some fields for the high time complexity. In this paper, a new image fusion method based on group sparse representation is proposed to overcome ...
Song, Zongxi +5 more
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Robust Sparse Representation for Incomplete and Noisy Data
Information, 2015 Owing to the robustness of large sparse corruptions and the discrimination of class labels, sparse signal representation has been one of the most advanced techniques in the fields of pattern classification, computer vision, machine learning and so on ...
Jiarong Shi, Xiuyun Zheng, Wei Yang
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Iterative thresholding for sparse approximations [PDF]
, 2008 Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimal sparse expansion is known to be NP hard in general and non-optimal strategies such as ...
Blumensath, T. +3 more
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Dictionary learning for sparse representation and classification of sound speed profile in the ocean [PDF]
, 2022 Badiey, MohsenWan, LinThe presence of internal waves (IWs) in the ocean alters the variability of sound speed profile (SSP) in the water column and plays an important role in applications such as underwater acoustics.
Castro-Correa, Jhon Alejandro
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On sparse evaluation representations
Theoretical Computer Science, 1997 The sparse evaluation graph has emerged over the past several years as an intermediate representation that captures the dataflow information in a program compactly and helps perform dataflow analysis efficiently. The contributions of this paper are three-fold: We present a linear time algorithm for constructing a variant of the sparse evaluation graph ...
openaire +1 more source
Block Orthonormal Overcomplete Dictionary Learning [PDF]
, 2013 In the field of sparse representations, the overcomplete dictionary learning problem is of crucial importance and has a growing application pool where it is used.
Dumitrescu, Bogdan, Rusu, Cristian
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Discriminative collaborative representation for multimodal image classification
International Journal of Advanced Robotic Systems, 2017 Sparse representation has been widely researched for image-based classification. However, sparse representation classification directly treats training samples as a dictionary, so it needs a large training set and is time consuming, especially for a ...
Dawei Sun +3 more
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Clustering before training large datasets - Case study: K-SVD [PDF]
, 2012 Training and using overcomplete dictionaries has been the subject of many developments in the area of signal processing and sparse representations. The main idea is to train a dictionary that is able to achieve good sparse representations of the items ...
Rusu, Cristian
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Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle
The Scientific World Journal, 2014 As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently ...
Xiangwei Xing +3 more
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Monte Carlo methods for adaptive sparse approximations of time-series [PDF]
, 2007 This paper deals with adaptive sparse approximations of time-series. The work is based on a Bayesian specification of the shift-invariant sparse coding model.
Michael E. Davies +3 more
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