N-way modeling for wavelet filter determination in Multivariate Image Analysis
[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 acquire...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/64793 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/64793 |
| Access Level: | acceso abierto |
| Palabra clave: | Wavelets Tucker3 Multivariate Image Analysis ESTADISTICA E INVESTIGACION OPERATIVA |
| Sumario: | [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. |
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