Texture detection for image analysis

Authors:
Sébastien Chabrier;Bruno Emile;Christophe Rosenberger
Affiliations:
Laboratoire Vision et Robotique, UPRES EA 2078, ENSI de Bourges – Université d'Orléans, Bourges, France;Laboratoire Vision et Robotique, UPRES EA 2078, ENSI de Bourges – Université d'Orléans, Bourges, France;Laboratoire Vision et Robotique, UPRES EA 2078, ENSI de Bourges – Université d'Orléans, Bourges, France
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

Citing 5
Cited 0

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Unsupervised Texture Segmentation in a Deterministic Annealing Framework

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Histogram clustering for unsupervised segmentation and image retrieval

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Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture

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Texture classification using logical operators

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Abstract

Many applications such as image compression, pre-processing or segmentation require some information from the regions composing an image. The main objective of this paper is to define a methodology to extract some local information from an image. Each region is characterized in terms of homogeneity (region composed with the same grey-level or a single texture) and its type (textured or uniform). The decision criterion is based on the use of classical texture attributes (cooccurrence matrix and grey-levels moments) and a support vector machine in order to realize the fusion of the different attributes. We then characterize each region considering its type by appropriate features.