Low-rank matrix fitting based on subspace perturbation analysis with applications to structure from motion

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 5, 2009, Pages 841-854

  Jia, Hongjun ^a   Martinez, Aleix M ^a  

^a Ohio State University   (United States)

Author keywords

Computer vision; Low rank matrix; Matrix perturbation; Missing data; Noise; Pattern recognition; Random matrix; Structure from motion; Subspace analysis

Indexed keywords

LOW-RANK MATRIX; MATRIX PERTURBATION; MISSING DATA; NOISE; RANDOM MATRIX; STRUCTURE FROM MOTION; SUBSPACE ANALYSIS;

COMPUTER VISION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; MOTION; MOVEMENT (PHYSIOLOGY); PHOTOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MOTION; MOVEMENT; PATTERN RECOGNITION, AUTOMATED; PHOTOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 64849112307     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.122     Document Type: Article

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