Particulate matter characterization by gray level co-occurrence matrix based support vector machines

Journal of Hazardous Materials

Volumn 223-224, Issue , 2012, Pages 94-103

  Manivannan, K ^a   Aggarwal, P ^a   Devabhaktuni, V ^a   Kumar, A ^b   Nims, D ^b   Bhattacharya, P ^c  

^a UNIVERSITY OF TOLEDO   (United States)

^b The University of Toledo   (United States)

^c University of Cincinnati   (United States)

Author keywords

Gray level co occurrence matrix; Image segmentation; Particulate matter; Support vector machines

Indexed keywords

AUTOMATIC SELECTION; GRAY LEVEL CO-OCCURRENCE MATRIX; HISTOGRAM TECHNIQUE; OPTIMAL SEGMENTATION ALGORITHM; PARTICULATE MATTER; SEGMENTATION ALGORITHMS; SPATIAL RELATIONSHIPS; SVM CLASSIFIERS; TEXTURE FEATURES; TRAINING DATA;

CLASSIFICATION (OF INFORMATION); GRAPHIC METHODS; IMAGE SEGMENTATION; IMAGE TEXTURE; NEURAL NETWORKS;

SUPPORT VECTOR MACHINES;

POLITEF;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DATA SET; HISTOGRAM; IMAGE CLASSIFICATION; PARTICULATE MATTER; PIXEL;

ARTICLE; ARTIFICIAL NEURAL NETWORK; GRAY LEVEL CO OCCURRENCE MATRIX; HISTOGRAM; IMAGE ANALYSIS; MATHEMATICAL MODEL; PARTICLE SIZE; PARTICULATE MATTER; SCANNING ELECTRON MICROSCOPY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

AIR POLLUTANTS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; ENVIRONMENTAL MONITORING; MICROSCOPY, ELECTRON, SCANNING; NEURAL NETWORKS (COMPUTER); PARTICLE SIZE; PARTICULATE MATTER; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84861457954     PISSN: 03043894     EISSN: 18733336     Source Type: Journal    
DOI: 10.1016/j.jhazmat.2012.04.056     Document Type: Article

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