Iterative Kernel Principal Component Analysis for image modeling

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 27, Issue 9, 2005, Pages 1351-1366

  Kim, Kwang In ^a   Franz, Matthias O ^b   Schölkopf, Bernhard ^b  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Image enhancement; Image models; Kernel methods; Principal component analysis; Unsupervised learning

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; EIGENVALUES AND EIGENFUNCTIONS; IMAGE ENHANCEMENT; IMAGE QUALITY; ITERATIVE METHODS; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS;

IMAGE MODELS; KERNEL METHODS; UNSUPERVISED LEARNING;

IMAGE PROCESSING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS;

EID: 25844445939     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2005.181     Document Type: Article

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