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IJAR
2006
89views more  IJAR 2006»
13 years 9 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
ICCV
2011
IEEE
12 years 9 months ago
Probabilistic Group-Level Motion Analysis and Scenario Recognition
This paper addresses the challenge of recognizing behavior of groups of individuals in unconstraint surveillance environments. As opposed to approaches that rely on agglomerative ...
Ming-Ching Chang, Nils Krahnstoever, Weina Ge
CIKM
2008
Springer
13 years 11 months ago
On effective presentation of graph patterns: a structural representative approach
In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the...
Chen Chen, Cindy Xide Lin, Xifeng Yan, Jiawei Han
DAGM
2010
Springer
13 years 9 months ago
Complex Motion Models for Simple Optical Flow Estimation
The selection of an optical flow method is mostly a choice from among accuracy, efficiency and ease of implementation. While variational approaches tend to be more accurate than lo...
Claudia Nieuwenhuis, Daniel Kondermann, Christoph ...
ICPR
2002
IEEE
14 years 10 months ago
A New Algorithm for Inexact Graph Matching
The graph is an essential data structure for representing relational information. When graphs are used to represent objects, comparing objects amounts to graph matching. Inexact g...
Adel Hlaoui, Shengrui Wang