Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Iterative Decoding Algorithm for Fusion of Multimodal Information | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 478396 10 pages doi 2008 478396 Research Article An Iterative Decoding Algorithm for Fusion of Multimodal Information Shankar T. Shivappa Bhaskar D. Rao and Mohan M. Trivedi Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA Correspondence should be addressed to Shankar T. Shivappa sshivappa@ Received 16 February 2007 Revised 19 July 2007 Accepted 26 October 2007 Recommended by Eric Pauwels Human activity analysis in an intelligent space is typically based on multimodal informational cues. Use of multiple modalities gives us a lot of advantages. But information fusion from different sources is a problem that has to be addressed. In this paper we propose an iterative algorithm to fuse information from multimodal sources. We draw inspiration from the theory of turbo codes. We draw an analogy between the redundant parity bits of the constituent codes of a turbo code and the information from different sensors in a multimodal system. A hidden Markov model is used to model the sequence of observations of individual modalities. The decoded state likelihoods from one modality are used as additional information in decoding the states ofthe other modalities. This procedure is repeated until a certain convergence criterion is met. The resulting iterative algorithm is shown to have lower error rates than the individual models alone. The algorithm is then applied to a real-world problem of speech segmentation using audio and visual cues. Copyright 2008 Shankar T. Shivappa et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTELLIGENT SPACES AND MULTIMODAL SYSTEMS Intelligent environments facilitate a natural and efficient
