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» Solving Geometric Covering Problems by Data Reduction
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CVPR
2006
IEEE
14 years 9 months ago
Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction
Geometric reconstruction problems in computer vision can be solved by minimizing the maximum of reprojection errors, i.e., the L-norm. Unlike L2-norm (sum of squared reprojection ...
Qifa Ke, Takeo Kanade
PODS
2008
ACM
128views Database» more  PODS 2008»
14 years 7 months ago
Shape sensitive geometric monitoring
A fundamental problem in distributed computation is the distributed evaluation of functions. The goal is to determine the value of a function over a set of distributed inputs, in ...
Izchak Sharfman, Assaf Schuster, Daniel Keren
WEBI
2010
Springer
13 years 5 months ago
DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Su Yan, Sofien Bouaziz, Dongwon Lee
PDCN
2004
13 years 9 months ago
Scalable parallel algorithms for difficult combinatorial problems: A case study in optimization
A novel combination of emergent algorithmic methods, powerful computational platforms and supporting infrastructure is described. These complementary tools and technologies are us...
Faisal N. Abu-Khzam, Michael A. Langston, Pushkar ...
DBVIS
1995
162views Database» more  DBVIS 1995»
13 years 11 months ago
LadMan: A Large Data Management System
More and more of our customers have to deal with very large datasets like elevation data and digital roadmaps covering Europe or even the entire world, very large images e.g. from...
Walter Schmeing