Treffer: Bayesian segmentation and clustering for determining cloud mask images
Title:
Bayesian segmentation and clustering for determining cloud mask images
Authors:
Source:
Opto-Ireland 2002 : optical metrology, imaging, and machine vision (Galway, 5-6 September 2002)SPIE proceedings series. :144-155
Publisher Information:
Bellingham WA: SPIE, 2003.
Publication Year:
2003
Physical Description:
print, 9 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Metrology and instrumentation, Métrologie et instrumentation, Optics, Optique, Physics, Physique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse composante principale, Principal component analysis, Análisis componente principal, Loi normale, Gaussian distribution, Curva Gauss, Modèle Markov, Markov model, Modelo Markov, Observation aérienne, Aerial survey, Observación aérea, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Segmentation image, Image segmentation, Transformation Karhunen Loeve, Karhunen Loeve transformation, Transformación Karhunen Loeve, Facteur Bayes, Image masque, Image multibande, Modèle mélange Gauss, Observation terre
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Computer Science, Queen's University Belfast, Belfast BT7 1NN, United Kingdom
Departamento de Señales y Comunicaciones, U.L.P.G.C, Campus Univ. de Tafira, 35017 Las Palmas de Gran Canaria, Spain
Departamento de Señales y Comunicaciones, U.L.P.G.C, Campus Univ. de Tafira, 35017 Las Palmas de Gran Canaria, Spain
Rights:
Copyright 2003 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.15171492
Database:
PASCAL Archive
Weitere Informationen
We assess both marginal density clustering, and spatial clustering using a Markov random field, on multiband Earth observation data. We use a Bayes factor assessment procedure in all cases. We find that the spatial model leads to better results, although the non-spatial clustering achieves a better false alarm rate.