Pattern generation using likelihood inference for cellular automata

IEEE Transactions on Image Processing

Volumn 15, Issue 7, 2006, Pages 1718-1727

  Craiu, Radu V ^a   Lee, Thomas C M ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Binary patterns; Cellular automata; Maximum likelihood estimation; Minimum description length principle; Neighborhood selection; Rule estimation; Stochastic cellular automata

Indexed keywords

AUTOMATA THEORY; COMPUTATIONAL METHODS; DATA DESCRIPTION; MAXIMUM LIKELIHOOD ESTIMATION; PATTERN RECOGNITION; RANDOM PROCESSES;

BINARY PATTERNS; CELLULAR AUTOMATA; MINIMUM DESCRIPTION LENGTH PRINCIPLE; NEIGHBORHOOD SELECTION; RULE ESTIMATION;

IMAGE PROCESSING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOMIMETICS; CELL FUNCTION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; SIGNAL PROCESSING; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMIMETICS; CELL PHYSIOLOGY; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 33745619837     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2006.873472     Document Type: Article

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