A class-adaptive spatially variant mixture model for image segmentation

IEEE Transactions on Image Processing

Volumn 16, Issue 4, 2007, Pages 1121-1130

  Nikou, Christophoros ^a   Galatsanos, Nikolaos P ^a   Likas, Aristidis C ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Clustering based image segmentation; Expectation maximization (EM) algorithm; Gauss Markov random field; Gaussian mixture model; Maximum a posteriori (MAP) estimation; Spatial smoothness constraints

Indexed keywords

CLUSTERING ALGORITHMS; ESTIMATION; GAUSSIAN DISTRIBUTION; MARKOV PROCESSES; PIXELS; PROBABILITY;

EXPECTATION-MAXIMIZATION ALGORITHM; GAUSS-MARKOV RANDOM FIELD; GAUSSIAN MIXTURE MODEL; MAXIMUM A POSTERIORI;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 34047208285     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2007.891771     Document Type: Article

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