Difference between revisions of "Skills"
From LRDE
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A common underlying problem of all the projects LRDE members work on is how to reconcile two aspects usually contradictory: performance and genericity. |
A common underlying problem of all the projects LRDE members work on is how to reconcile two aspects usually contradictory: performance and genericity. |
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Revision as of 17:53, 22 July 2013
This page quickly presents the skills and research projects of the LRDE. For more detailed information, please go to the projects page.
Skills
The activities of LRDE cover two main areas of research:
- Image and pattern recognition and
- Automata and verification
with a transverse research axis Performance and genericity
Each main area, or theme, is divided into two fields.
Theme 1: Image and pattern recognition
Image processing
Image processing at LRDE consists mainly of writing morphological algorithms. The principal fields of application are document image analysis and medical image processing. The state of the art of these elements as well as all the results of the Image-team's research have been integrated in Olena, a high performance generic image processing library.
Speaker ID and signal processing
In the field of speaker recognition, the LRDE works on issues related to channel effects, the variability of the speaker and, more generally, to mismatch conditions between training and test speech utterances. The Speaker ID-team proposes low-dimensional representations of speaker identities.
Theme 2: Automata and verification
Multiple finite-state machines
LRDE's Automata-team develops Vaucanson, a platform for the manipulation of multiple finite-state machines in order to provide genericity, efficiency, respect of the theory and multipurpose tools.
Formal verification
The second aspect of the team's research concerns automata on infinite words (ω-words) used for formal verification or model checking. The Spot library has been designed for this.
Transverse research axis
A common underlying problem of all the projects LRDE members work on is how to reconcile two aspects usually contradictory: performance and genericity.