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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29137


    Title: A genetic algorithm for MRF-based segmentation of multi-spectral textured images
    Authors: Tseng,DC;Lai,CC
    Contributors: 資訊工程研究所
    Keywords: MARKOV RANDOM-FIELD;UNSUPERVISED SEGMENTATION;COLOR IMAGES;CLASSIFICATION;RELAXATION;MODELS
    Date: 1999
    Issue Date: 2010-06-29 20:14:36 (UTC+8)
    Publisher: 中央大學
    Abstract: A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. Tn this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multispectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images. (C) 1999 Elsevier Science B.V. All rights reserved.
    Relation: PATTERN RECOGNITION LETTERS
    Appears in Collections:[資訊工程研究所] 期刊論文

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