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JAIR
2002
120views more  JAIR 2002»
13 years 7 months ago
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
Hagit Shatkay, Leslie Pack Kaelbling
WSCG
2004
118views more  WSCG 2004»
13 years 8 months ago
New Method for Geometric Constraint Solving Based on the Genetic Quantum Algorithm
This paper proposes a novel genetic quantum algorithm (GQA) to solve geometric constraint problems. Instead of binary, numeric or symbolic representation, we introduce qubit chrom...
Chunhong Cao, Wenhui Li
EGH
2009
Springer
13 years 5 months ago
Efficient stream compaction on wide SIMD many-core architectures
Stream compaction is a common parallel primitive used to remove unwanted elements in sparse data. This allows highly parallel algorithms to maintain performance over several proce...
Markus Billeter, Ola Olsson, Ulf Assarsson
ICCV
2005
IEEE
14 years 9 months ago
Geometric Context from a Single Image
Many computer vision algorithms limit their performance by ignoring the underlying 3D geometric structure in the image. We show that we can estimate the coarse geometric propertie...
Derek Hoiem, Alexei A. Efros, Martial Hebert
WAE
2001
223views Algorithms» more  WAE 2001»
13 years 8 months ago
An Adaptable and Extensible Geometry Kernel
Geometric algorithms are based on geometric objects such as points, lines and circles. The term Kernel refers to a collection of representations for constant-size geometric objects...
Susan Hert, Michael Hoffmann, Lutz Kettner, Sylvai...