Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks

Meat Science

Volumn 87, Issue 2, 2011, Pages 107-114

  Iqbal, Abdullah ^a   Valous, Nektarios A ^a   Sun, Da Wen ^a   Allen, Paul ^b  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b ASHTOWN FOOD RESEARCH CENTRE   (Ireland)

Author keywords

Chi square; Computer vision; Gliding box binary lacunarity; Mutual information; Pork ham; Salient features; Supervised classification

Indexed keywords

CHI-SQUARE; GLIDING BOX BINARY LACUNARITY; MUTUAL INFORMATIONS; PORK HAM; SALIENT FEATURES; SUPERVISED CLASSIFICATION;

COMPUTER VISION; FEATURE EXTRACTION; MEATS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

CLASSIFICATION (OF INFORMATION);

ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; COMPUTER PROGRAM; MEAT; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL; SWINE;

ANIMALS; MEAT; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SOFTWARE; SWINE;

EID: 78649630708     PISSN: 03091740     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.meatsci.2010.08.014     Document Type: Article

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