A generative probabilistic oriented wavelet model for texture segmentation
- Author
- STAINVAS, Inna1 ; LOWE, David2
[1] Orbotech Ltd., P.O. Box 215, Yavne 81102, Israel
[2] Neural Computing Research Group, Information Engineering, Asian University, United Kingdom - Source
Neural processing letters. 2003, Vol 17, Num 3, pp 217-238, 22 p ; ref : 41 ref
- ISSN
- 1370-4621
- Scientific domain
- Computer science
- Publisher
- Springer, Dordrecht
- Publication country
- Netherlands
- Document type
- Article
- Language
- English
- Author keyword
- Bayesian networks and ensembles Dyadic wavelet transform Gabor wavelet transform generative probabilistic model image texture segmentation novelty detection
- Keyword (fr)
- Faisabilité Interconnexion Lettre alphabet Modèle Markov caché Modèle probabiliste Nouveauté Réseau Bayes Segmentation image Texture image Théorie variable cachée Traitement image Transformation ondelette Transformation ondelette Gabor Transformation ondelette dyadique
- Keyword (en)
- Feasibility Interconnection Letter Hidden Markov model Probabilistic model Novelty Bayes network Image segmentation Image texture Hidden variable theory Image processing Wavelet transformation
- Keyword (es)
- Practicabilidad Interconexión Letra alfabeto Modelo Markov oculto Modelo probabilista Novedad Red Bayes Teoría variable escondida Procesamiento imagen Transformación ondita
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02C Artificial intelligence / 001D02C03 Pattern recognition. Digital image processing. Computational geometry
- Discipline
- Computer science : theoretical automation and systems
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 15223888
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
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