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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/74477


    Title: Commensurate dimensionality reduction for extended local ternary patterns
    Authors: Liao, Wen-Hung
    廖文宏
    Contributors: 資科系
    Keywords: Comparative analysis;Contributory factors;Descriptors;Dimensionality reduction;Feature vectors;Image sets;Local ternary patterns;Noise resistance;Non-uniform patterns;Texture classification;Software engineering;Pattern recognition
    Date: 2012
    Issue Date: 2015-04-10 16:38:23 (UTC+8)
    Abstract: We present a systematic approach to reduce the dimensionality of the feature vector for local binary/ternary patterns. The proposed framework examines the distribution of uniform patterns in different image sets to formulate a procedure to assign dimensionality to uniform and non-uniform patterns. Unlike previous methods where all the information from non-uniform patterns is discarded or merged into a single dimension, the proposed commensurate dimensionality reduction (CDR) technique attempts to retain valuable information from all contributory factors. Experiments and comparative analysis have validated the efficacy of the newly defined CDR-ELTP descriptor in terms of noise resistance and texture classification. © 2012 ICPR Org Committee.
    Relation: Proceedings - International Conference on Pattern Recognition
    Data Type: conference
    Appears in Collections:[Department of Computer Science ] Proceedings

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