Hierarchical Finite-State Modeling for Texture Segmentation with Application to Forest Classification

Giuseppe Scarpa 1 Michal Haindl 2 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
Abstract : In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- and inter-texture interactions, and eventually addresses the segmentation task in a completely unsupervised manner. Moreover, it provides a hierarchical output, as the user may decide the scale at which the segmentation has to be given. Tests were carried out on both natural texture mosaics provided by the Prague Texture Segmentation Datagenerator Benchmark and remote-sensing data of forest areas provided by the French National Forest Inventory (IFN).
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Rapport
[Research Report] RR-6066, INRIA. 2006, pp.47
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Giuseppe Scarpa, Michal Haindl, Josiane Zerubia. Hierarchical Finite-State Modeling for Texture Segmentation with Application to Forest Classification. [Research Report] RR-6066, INRIA. 2006, pp.47. 〈inria-00118420v2〉

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