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    FOCM
    2002
    97views more  FOCM 2002»
    On the Riemannian Geometry Defined by Self-Concordant Barriers and Interior-Point Methods
    13 years 7 months ago
    On the Riemannian Geometry Defined by Self-Concordant Barriers and Interior-Point Methods
    Download  ecommons.library.cornell.edu
    We consider the Riemannian geometry defined on a convex set by the Hessian of a selfconcordant barrier function, and its associated geodesic curves. These provide guidance for the...
    Yu. E. Nesterov, Michael J. Todd
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    ICDE
    2007
    IEEE
    112views Database» more  ICDE 2007»
    Indexing Point Triples Via Triangle Geometry
    14 years 9 months ago
    Indexing Point Triples Via Triangle Geometry
    Download  www.cs.umd.edu
    Database search for images containing icons with specific mutual spatial relationships can be facilitated by an appropriately structured index. For the case of images containing s...
    Charles Ben Cranston, Hanan Samet
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    JISE
    2007
    54views more  JISE 2007»
    Reversible Data Hiding for Point-Sampled Geometry
    13 years 7 months ago
    Reversible Data Hiding for Point-Sampled Geometry
    Download  www.iis.sinica.edu.tw
    Peng-Cheng Wang, Chung-Ming Wang
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    ISMAR
    2003
    IEEE
    123views Augmented Reality» more  ISMAR 2003»
    SenseShapes: Using Statistical Geometry for Object Selection in a Multimodal Augmented Reality System
    14 years 1 months ago
    SenseShapes: Using Statistical Geometry for Object Selection in a Multimodal Augmented Reality System
    Download  www.hrvojebenko.com
    We introduce a set of statistical geometric tools designed to identify the objects being manipulated through speech and gesture in a multimodal augmented reality system. SenseShap...
    Alex Olwal, Hrvoje Benko, Steven Feiner
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    JMLR
    2010
    132views more  JMLR 2010»
    Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
    13 years 2 months ago
    Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
    Download  ftp.isds.duke.edu
    The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
    Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mu...
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