Research on the classification of wood texture based on Gray Level Co-occurrence Matrix

Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

Volumn 37, Issue 12, 2005, Pages 1667-1670

  Bai, Xue Bing ^a   Wang, Ke Qi ^a   Wang, Hui ^a  

^a NORTHEAST FORESTRY UNIVERSITY   (China)

Author keywords

Competitive neural networks; Feature parameter; Gray level co occurrence matrix; Wood texture

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; MATRIX ALGEBRA; NEURAL NETWORKS;

BUILDING RULES; COMPETITIVE NEURAL NETWORKS; FEATURE PARAMETER; GRAY LEVEL COOCCURRENCE MATRIX (GLCM); RELEVANCE ANALYSIS; SURFACE TEXTURE; WOOD IMAGE SIZE; WOOD TEXTURE;

WOOD;

CLASSIFICATION; IMAGE ANALYSIS; TEXTURE; WOOD STRUCTURE;

EID: 33344476971     PISSN: 03676234     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (7)

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3. BARALDI A, PARMIGGIAN F. An investigation of texture characteristics associated with gray level co-occurrence matrix statistical parameters [J]. IEEE Trans. On Geo-science and Remote sensing, 1995, 33(2): 293-303.
4. POTILLE G J, TRUEBA S I, De MIGUEL V G. Efficient Multi-spectral texture segmentation using multivariate statistics [J]. IEEE Proceeding, Vision Image, and Signal Processing, 1998, 145(5): 357-364.