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Unsupervised Surface Reflectance Field Multi-segmenter
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SYSNO ASEP 0447050 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Unsupervised Surface Reflectance Field Multi-segmenter Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Mikeš, Stanislav (UTIA-B) RID
Kudo, M. (JP)Number of authors 3 Source Title Computer Analysis of Images and Patterns - CAIP 2015, I. - Switzerland : Springer International Publishing, 2015 / Azzopardi George ; Petkov Nicolai - ISSN 0302-9743 - ISBN 978-3-319-23192-1 Pages s. 261-273 Number of pages 13 s. Publication form Print - P Action 16th International Conference on Computer Analysis of Images and Patterns Event date 02.09.2015-04.09.2015 VEvent location Valletta Country MT - Malta Event type WRD Language eng - English Country CH - Switzerland Keywords Unsupervised image segmentation ; Textural features ; Illumination invariants ; Surface reflectance field ; Bidirectional texture function Subject RIV BD - Theory of Information R&D Projects GA14-10911S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000364705500022 EID SCOPUS 84945931089 DOI 10.1007/978-3-319-23192-1_22 Annotation An unsupervised, illumination invariant, multi-spectral, mul/-ti-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by eight causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark both on the surface reflectance field textures as well as on the static colour textures using the commonest segmentation criteria and compares favourably with several leading alternative image segmentation methods. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2016
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