Low-Rank Matrix Fitting Based on Subspace Perturbation Analysis with Applications to Structure from Motion - Hongjun Jia; Martinez, A.M. (IEEE transactions on pattern analysis and machine intelligence, 2009)
Keywords: Additive noise; Algorithms; Applied sciences; Artificial Intelligence; Bioinformatics; Computer graphics; Computer science; control theory; systems; Computer vision; Criteria; Data engineering; Exact sciences and technology; Fittings; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Intelligence; Low-rank matrix; Mathematical analysis; Matrices; Matrix methods; matrix perturbation; missing data; Motion; Motion analysis; Movement; Noise; Optical noise; Pattern analysis; Pattern recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Photography - methods; random matrix; Reproducibility of Results; Sensitivity and Specificity; structure from motion; Studies; subspace analysis; Upper bound; noise; matrix perturbation; subspace analysis; structure from motion; Additive noise; Occlusion; Validation; Computer vision; Random matrix; Low-rank matrix; Pattern recognition; Modeling; Missing data; Upper bound; Perturbation method; Vector space; Incomplete information; Pattern analysis; Artificial intelligence; Occultation