A spatially constrained generative asymmetric Gaussian mixture model for image segmentation

Journal of Visual Communication and Image Representation

Volumn 40, Issue , 2016, Pages 611-626

  Ji, Zexuan ^a   Huang, Yubo ^a   Sun, Quansen ^a   Cao, Guo ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Asymmetric Gaussian mixture model; Expectation maximization (EM) algorithm; Image segmentation; Markov random fields (MRFs); Spatial constraint

Indexed keywords

APPROXIMATION ALGORITHMS; COMMUNICATION CHANNELS (INFORMATION THEORY); GAUSSIAN DISTRIBUTION; IMAGE PROCESSING; MARKOV PROCESSES; MAXIMUM PRINCIPLE; OBJECT RECOGNITION;

ASYMMETRIC DISTRIBUTION; EXPECTATION-MAXIMIZATION ALGORITHMS; GAUSSIAN MIXTURE MODEL; KULLBACK LEIBLER DIVERGENCE; MARKOV RANDOM FIELD; ROBUSTNESS TO NOISE; SPATIAL CONSTRAINTS; SPATIAL INFORMATIONS;

IMAGE SEGMENTATION;

EID: 84991101931     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2016.08.001     Document Type: Article

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