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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:	[資訊科學系] 會議論文

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