Nonparametric statistical snake based on the minimum stochastic complexity

IEEE Transactions on Image Processing

Volumn 15, Issue 9, 2006, Pages 2762-2770

  Martin, Pascal ^a,b   Réfrégier, Philippe ^a   Galland, Frédéric ^a   Guérault, Frédéric ^b  

^a AIX MARSEILLE UNIVERSITÉ   (France)

^b Simag Développement   (France)

Author keywords

Image segmentation; Level set; Minimum description length principle; Snakes; Stochastic complexity

Indexed keywords

OPTIMIZATION; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES; STATISTICAL METHODS;

LEVEL SET; MINIMUM DESCRIPTION LENGTH PRINCIPLE; STATISTICAL SNAKE; STOCHASTIC COMPLEXITY;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; STOCHASTIC PROCESSES;

EID: 33747659641     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2006.877317     Document Type: Article

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