Multimedia multimodal methodologies

Authors:
L. Guan;P. Muneesawang;Y. Wang;R. Zhang;Y. Tie;A. Bulzacki;M. T. Ibrahim
Affiliations:
Ryerson Multimedia Laboratory, Ryerson University, Toronto, Canada;Naresuan University, Thailand;Department of Electrical and Computer Engineering, University of Toronto, Canada;Ryerson Multimedia Laboratory, Ryerson University, Toronto, Canada;Ryerson Multimedia Laboratory, Ryerson University, Toronto, Canada;Ryerson Multimedia Laboratory, Ryerson University, Toronto, Canada;Ryerson Multimedia Laboratory, Ryerson University, Toronto, Canada
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 19
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Abstract

This paper outlines several multimedia systems that utilize a multimodal approach. These systems include audiovisual based emotion recognition, image and video retrieval, and face and head tracking. Data collected from diverse sources/sensors are employed to improve the accuracy of correctly detecting, classifying, identifying, and tracking of a desired object or target. It is shown that the integration of multimodality data will be more efficient and potentially more accurate than if the data was acquired from a single source. A number of cutting-edge applications for multimodal systems will be discussed. An advanced assistance robot using the multimodal systems will be presented.