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» Learning and Inference with Constraints
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ICCV
2003
IEEE
16 years 8 months ago
Tracking Articulated Body by Dynamic Markov Network
A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendou...
Ying Wu, Gang Hua, Ting Yu
CPAIOR
2010
Springer
15 years 4 months ago
Lazy Clause Generation: Combining the Power of SAT and CP (and MIP?) Solving
Finite domain propagation solving, the basis of constraint programming (CP) solvers, allows building very high-level models of problems, and using highly specific inference encapsu...
Peter J. Stuckey
CORR
2010
Springer
167views Education» more  CORR 2010»
15 years 4 months ago
Compressive Sensing over Graphs
In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are ...
Weiyu Xu, Enrique Mallada, Ao Tang
TOG
2012
273views Communications» more  TOG 2012»
13 years 9 months ago
Functional maps: a flexible representation of maps between shapes
We present a novel representation of maps between pairs of shapes that allows for efficient inference and manipulation. Key to our approach is a generalization of the notion of m...
Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, ...
CONTEXT
1999
Springer
15 years 11 months ago
The Pragmatic Roots of Context
When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant...
Bruce Edmonds