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ICML
2004
IEEE
16 years 4 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
IPMI
2011
Springer
14 years 6 months ago
Actin Filament Segmentation Using Dynamic Programming
Abstract. We introduce a novel algorithm for actin filament segmentation in 2D TIRFM image sequences. This problem is difficult because actin filaments dynamically change shapes ...
Hongsheng Li, Tian Shen, Xiaolei Huang
ICCV
2003
IEEE
16 years 5 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
121
Voted
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
15 years 10 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun
ICSE
1999
IEEE-ACM
15 years 7 months ago
Dynamically Discovering Likely Program Invariants to Support Program Evolution
ÐExplicitly stated program invariants can help programmers by identifying program properties that must be preserved when modifying code. In practice, however, these invariants are...
Michael D. Ernst, Jake Cockrell, William G. Griswo...