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Xianju Zeng - One of the best experts on this subject based on the ideXlab platform.

  • image fusion using higher order Singular Value Decomposition
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Junli Liang, Yang He, Xianju Zeng
    Abstract:

    A novel higher order Singular Value Decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute Values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.

Junli Liang - One of the best experts on this subject based on the ideXlab platform.

  • image fusion using higher order Singular Value Decomposition
    IEEE Transactions on Image Processing, 2012
    Co-Authors: Junli Liang, Yang He, Xianju Zeng
    Abstract:

    A novel higher order Singular Value Decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute Values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.

Yann Sooning - One of the best experts on this subject based on the ideXlab platform.

Lm Rocha - One of the best experts on this subject based on the ideXlab platform.

  • Singular Value Decomposition and principal component analysis
    A Practical Approach to Microarray Data Analysis, 2003
    Co-Authors: Me Wall, Andreas Rechtsteiner, Lm Rocha
    Abstract:

    This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.

James Hofrichter - One of the best experts on this subject based on the ideXlab platform.

  • 8 Singular Value Decomposition application to analysis of experimental data
    Methods in Enzymology, 1992
    Co-Authors: Eric R Henry, James Hofrichter
    Abstract:

    Publisher Summary This chapter summarizes the properties of the Singular Value Decomposition, which are relevant for data analysis. The chapter describes the way in which the Singular Value Decomposition (SVD) of a noise-free data set for which the spectra, f , and concentration, c , vectors are known can be calculated from consideration of the integrated overlaps of these components. Because data analysis necessarily begins with matrices, which are “noisy” at some level of precision, the chapter describes some of the properties of the SVD of matrices, which contain noise. It describes the SVD of random matrices (that is, matrices containing only noise). The chapter explores the way the random amplitudes are distributed in the SVD output when noise is added to a data matrix by using perturbation theory. The chapter discusses the significant advantages that result from the application of SVD and complementary processing techniques for the analysis of large sets of experimental data.