Archetypal analysis: an alternative to clustering for unsupervised texture segmentation
Metadatos
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comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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INVESTIGACIONMetadatos
Título
Archetypal analysis: an alternative to clustering for unsupervised texture segmentationFecha de publicación
2019-04Editor
International Society for Stereology & Image AnalysisCita bibliográfica
CABERO, I.; EPIFANIO, I. Archetypal analysis in texture segmentation. Image Anal. Stereol., 2019, 38: 151-160Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.ias-iss.org/ojs/IAS/article/view/2052Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the ... [+]
Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The proposed procedure has been tested on a remote sensing image application with local granulometries, providing promising results. [-]
Proyecto de investigación
Spanish Ministerio de Ciencia, Innovacion y Universidades (AEI/FEDER, UE) (Grant DPI2017-87333-R) ; Universitat Jaume I (UJIB2017-13)Derechos de acceso
info:eu-repo/semantics/openAccess
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- IF_Articles [318]
- MAT_Articles [749]
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