Premium
N ‐way modeling for wavelet filter determination in multivariate image analysis
Author(s) -
PratsMontalbán José Manuel,
Cocchi Marina,
Ferrer Alberto
Publication year - 2015
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2717
Subject(s) - wavelet , image (mathematics) , computer science , artificial intelligence , filter (signal processing) , pattern recognition (psychology) , range (aeronautics) , multiresolution analysis , exploratory analysis , task (project management) , multivariate statistics , wavelet transform , data mining , machine learning , discrete wavelet transform , computer vision , data science , engineering , systems engineering , aerospace engineering
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.