Authors:
Thomas Burger
1
;
Alexandra Urankar
1
;
Oya Aran
2
;
Lale Akarun
2
and
Alice Caplier
3
Affiliations:
1
France Telecom R&D, France
;
2
Bogazici University, Turkey
;
3
LIS, Institut National Polytechnique de Grenoble, France
Keyword(s):
Support Vector Machine, Expert systems, Belief functions, Hu invariants, Hand shape and gesture recognition, Cued Speech.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
As part of our work on hand gesture interpretation, we present our results on hand shape recognition. Our method is based on attribute extraction and multiple binary SVM classification. The novelty lies in the fashion the fusion of all the partial classification results are performed. This fusion is (1) more efficient in terms of information theory and leads to more accurate result, (2) general enough to allow other source of information to be taken into account: Each SVM output is transformed to a belief function, and all the corresponding functions are fused together with some other external evidential sources of information.