Learning to match: Deriving optimal template-matching algorithms from probabilistic image models

International Journal of Computer Vision

Volumn 88, Issue 2, 2010, Pages 189-213

  Vidal, Camille ^a   Jedynak, Bruno ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

Anatomical landmark detection; Deformable template; Image registration; Statistical learning

Indexed keywords

ANATOMICAL LANDMARKS; AUTOMATIC DETECTION; BRAIN MAGNETIC RESONANCE IMAGES; COMMON PROBLEMS; DEFORMABLE TEMPLATES; GAUSSIANS; GRAY-SCALE IMAGES; INTENSITY DISTRIBUTION; MATCHING ALGORITHM; MATCHING FUNCTIONS; PHOTOMETRIC VARIATIONS; PROBABILISTIC IMAGES; PROBABILISTIC MODELS; STATISTICAL FRAMEWORK; STATISTICAL LEARNING; STATISTICAL MODELS; TEMPLATE-MATCHING ALGORITHMS; TRAINING EXAMPLE;

BRAIN MODELS; COST FUNCTIONS; DEFORMATION; IMAGE REGISTRATION; LEARNING ALGORITHMS; MAGNETIC RESONANCE IMAGING; OPTIMIZATION; RANDOM PROCESSES; RANDOM VARIABLES; RESONANCE; TEMPLATE MATCHING;

IMAGE MATCHING;

EID: 77951296739     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-009-0258-5     Document Type: Article

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