Parameterized kernel principal component analysis: Theory and applications to supervised and unsupervised image alignment

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  De La Torre, Fernando ^a   Minh, Hoai Nguyen ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; FACE RECOGNITION; FEATURE EXTRACTION; FINANCIAL DATA PROCESSING; IMAGE PROCESSING; IMAGE REGISTRATION; MODAL ANALYSIS; PATTERN RECOGNITION;

ACTIVE APPEARANCE MODELS; APPEARANCE MODELING; BEST MATCH; IMAGE ALIGNMENT; IMPROVED PERFORMANCE; KERNEL PCA; KERNEL PRINCIPAL COMPONENT ANALYSIS; LABELED TRAINING DATA; MATRIX FORMULATION; MORPHABLE MODELS; NON-LINEAR; PARAMETERIZED; PRINCIPAL COMPONENT ANALYSIS (PCA);

PRINCIPAL COMPONENT ANALYSIS;

EID: 51949111955     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587523     Document Type: Conference Paper

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