N-way modeling for wavelet filter determination in Multivariate Image Analysis

Additional information may be found in the online version of this article at the publisher’s web site[EN] When trying to analyze spatial relationships in image analysis, wavelets appear as one of the state-of-the-art tools. However, image analysis is a problem-dependent issue, and different applications might require different wavelets in order to gather the main sources of variation in the acquired images with respect to the specific task to be performed. This paper provides a methodology based on N-way modeling for properly selecting the best wavelet choice to use or at least to provide a range of possible wavelet choices (in terms of families, filters, and decomposition levels), for each image and problem at hand. The methodology has been applied on two different data sets with exploratory and monitoring objectives. Copyright © 2015 John Wiley & Sons, Ltd.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Prats-Montalbán, JM.; Cocchi, M.; Ferrer Riquelme, AJ. (2015). N-way modeling for wavelet filter determination in Multivariate Image Analysis. Journal of Chemometrics. 29:379-388. https://doi.org/10.1002/cem.2717S37938829Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Liu, J. J., & MacGregor, J. F. (2007). On the extraction of spectral and spatial information from images. Chemometrics and Intelligent Laboratory Systems, 85(1), 119-130. doi:10.1016/j.chemolab.2006.05.011Liu, J. J., & MacGregor, J. F. (2006). Estimation and monitoring of product aesthetics: application to manufacturing of «engineered stone» countertops. 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Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

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Vector extension of monogenic wavelets for geometric representation of color images

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Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images

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Multispectral scleral patterns for ocular biometric recognition

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