ICRA
2008
IEEE
14 years 6 months ago
2008
IEEE
— We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination...
ICASSP
2008
IEEE
14 years 6 months ago
2008
IEEE
We are interested in recovering aspects of vocal tract’s geometry and dynamics from auditory and visual speech cues. We approach the problem in a statistical framework based on ...
ICASSP
2008
IEEE
14 years 6 months ago
2008
IEEE
In this paper, we propose a novel multi-dimensional distributed hidden Markov model (DHMM) framework. We first extend the theory of 2D hidden Markov models (HMMs) to arbitrary ca...
FGR
2008
IEEE
14 years 6 months ago
2008
IEEE
We examine in detail some properties of gesture recognition models which utilize a reduced number of parameters and lower algorithmic complexity compared to traditional hidden Mar...
ICDAR
2009
IEEE
14 years 6 months ago
2009
IEEE
Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...
PERCOM
2006
ACM
14 years 11 months ago
2006
ACM
This paper addresses the problem of implementing an context model. First, the abstract context model is represented by a network of situations. Two different implementations for t...
ICIAP
2003
ACM
15 years 7 days ago
2003
ACM
SIGMOD
2008
ACM
15 years 8 days ago
2008
ACM
A major problem in detecting events in streams of data is that the data can be imprecise (e.g. RFID data). However, current state-ofthe-art event detection systems such as Cayuga ...
RECOMB
2003
Springer
15 years 13 days ago
2003
Springer
A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process c...
ICML
1999
IEEE
15 years 28 days ago
1999
IEEE
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...