• DocumentCode
    2936658
  • Title

    Multimedia multimodal methodologies

  • Author

    Guan, L. ; Muneesawang, P. ; Wang, Y. ; Zhang, R. ; Tie, Y. ; Bulzacki, A. ; Ibrahim, M.T.

  • Author_Institution
    Ryerson Multimedia Lab., Ryerson Univ., Toronto, ON, Canada
  • fYear
    2009
  • fDate
    June 28 2009-July 3 2009
  • Firstpage
    1600
  • Lastpage
    1603
  • 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.
  • Keywords
    emotion recognition; face recognition; image classification; multimedia computing; object recognition; tracking; video retrieval; audiovisual based emotion recognition; cutting-edge application; diverse source/sensor; face recognition; head tracking; image retrieval; multimedia multimodal methodology; video retrieval; Data mining; Emotion recognition; Feature extraction; Fingerprint recognition; Fusion power generation; Multimedia systems; Noise level; Pattern recognition; Principal component analysis; Target tracking; Data fusion; multimedia; multimodal; pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on
  • Conference_Location
    New York, NY
  • ISSN
    1945-7871
  • Print_ISBN
    978-1-4244-4290-4
  • Electronic_ISBN
    1945-7871
  • Type

    conf

  • DOI
    10.1109/ICME.2009.5202824
  • Filename
    5202824