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Showing results 1385 to 1404 of 2719 < previous   next >
PreviewIssue DateTitleAuthor(s)
2010LACBoost and FisherBoost: optimally building cascade classifiersShen, C.; Wang, P.; Li, H.; European Conference on Computer Vision (ECCV) (10th : 2010 : Crete, Greece)
2008Lambda calculus as a workflow modelKelly, P.; Coddington, P.; Wendelborn, A.; International Conference on Grid and Pervasive Computing Workshop (3rd : 2008 : Kunming, China)
2009Lambda calculus as a workflow modelKelly, P.; Coddington, P.; Wendelborn, A.
2011Laplacian margin distribution boosting for learning from sparsely labeled dataWang, T.; He, X.; Shen, C.; Barnes, N.; Digital Image Computing Techniques and Applications (2011 : Noosa, Qld.)
2014Large-margin learning of compact binary image encodingsPaisitkriangkrai, S.; Shen, C.; Van Den Hengel, A.
2017Large-scale binary quadratic optimization using semidefinite relaxation and applicationsWang, P.; Shen, C.; Van Den Hengel, A.; Torr, P.
2017Large-scale camera network topology estimation by lighting variationZhu, M.; Dick, A.; van den Hengel, A.; 18th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2017) (18 Sep 2017 - 21 Sep 2017 : Antwerp, Belgium)
2014Large-scale camera topology mapping: application to re-identificationDick, A.; Hengel, A.; Detmold, H.
2015Last-level cache side-channel attacks are practicalLiu, F.; Yarom, Y.; Ge, Q.; Heiser, G.; Lee, R.; Security and Privacy (SP) (17 May 2015 - 21 May 2015 : San Jose, CA)
2013Latent data association: Bayesian model selection for multi-target trackingSegal, A.; Reid, I.; International Conference on Computer Vision (2013 : Sydney, Australia)
2003Lattice QCD, gauge fixing and the transition to the perturbative regimeWilliams, A.; International Conference on Quark Confinement and the Hadron Spectrum (5th : 2002 : Gargnano, Italy)
2000Layer extraction with a bayesian model of shapesTorr, P.; Dick, A.; Cipolla, R.; European Conference on Computer Vision (ECCV) (26 Jun 2000 - 01 Jul 2000 : Dublin)
2013Learning a hybrid similarity measure for image retrievalWu, J.; Shen, H.; Li, Y.; Xiao, Z.; Lu, M.; Wang, C.
2017Learning a no-reference quality metric for single-image super-resolutionMa, C.; Yang, C.-.Y.; Yang, X.; Yang, M.-.H.
2007Learning and matching of dynamic shape manifolds for human action recognitionWang, L.; Suter, D.
2008Learning cascaded reduced - set SVMs using linear programmingKim, J.; Shen, C.; Wang, L.; International Conference on Digital Image Computing - Techniques and Applications, (2008 : Canberra, ACT, Australia)
2013Learning compact binary codes for visual trackingLi, X.; Shen, C.; Dick, A.; Van Den Hengel, A.; IEEE Conference on Computer Vision and Pattern Recognition (26th : 2013 : Portland, Oregon)
2019Learning deeply supervised good features to match for dense monocular reconstructionWeerasekera, C.; Garg, R.; Latif, Y.; Reid, I.; Asian Conference on Computer Vision (ACCV) (02 Dec 2018 - 06 Dec 2018 : Perth, Australia)
2016Learning depth from single monocular images using deep convolutional neural fieldsLiu, F.; Shen, C.; Lin, G.; Reid, I.
2016Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging dataZhou, L.; Wang, L.; Liu, L.; Ogunbona, P.; Shen, D.