Treffer: Texture features and segmentation based on multifractal approach
Title:
Texture features and segmentation based on multifractal approach
Authors:
Source:
Progress in pattern recognition, image analysis and applications (11th Iberoamerican congress in pattern recognition, CIARP 2006, Cancun, Mexico, November 14-17, 2006)0CIARP 2006. :297-305
Publisher Information:
Berlin; Heidelberg; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 24 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, 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, Algorithme k moyenne, K means algorithm, Algoritmo k media, Analyse amas, Cluster analysis, Analisis cluster, Analyse image, Image analysis, Análisis imagen, Analyse spectrale, Spectral analysis, Análisis espectral, Analyse spectre, Spectrum analysis, Análisis espectro, Analyse texture, Texture analysis, Análisis textura, Classification, Clasificación, Estimation densité, Density estimation, Estimación densidad, Fractal, Grande déviation, Large deviation, Gran desviación, Mesure densité, Density measurement, Medición densidad, Polynôme Legendre, Legendre polynomial, Polinomio Legendre, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Segmentation image, Image segmentation, Texturation, Texturación, Texture image, Image texture, Traitement image, Image processing, Procesamiento imagen
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
GRIMAAG UAG, Campus de Fouillole, French West Indies University, 97157 Pointe-à-Pitre Guadeloupe, France
ISSN:
0302-9743
Rights:
Copyright 2007 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.19078962
Database:
PASCAL Archive
Weitere Informationen
In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.