Gaussian MRF rotation-invariant features for image classification

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 26, Issue 7, 2004, Pages 951-955

  Deng, Huawu ^a   Clausi, David A ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Anisotropic; Classification; Discrete fourier transform (DFT); Gaussian MRF (GMRF) model; Isotropic; Least squares estimate (LSE); Markov random field (MRF); Rotational invariance; Texture analysis

Indexed keywords

DISCRETE FOURIER TRANSFORMS; IMAGE ANALYSIS; LAPLACE TRANSFORMS; LEAST SQUARES APPROXIMATIONS; MARKOV PROCESSES; MATHEMATICAL MODELS;

LAPLACIAN PYRAMID; MARKOV RANDOM FIELD; ROTATIONAL INVARIANCE; TEXTURE ANALYSIS;

PATTERN RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; METHODOLOGY; NORMAL DISTRIBUTION; PROBABILITY; ROTATION; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MARKOV CHAINS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; ROTATION;

EID: 3042673914     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2004.30     Document Type: Article

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