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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mu-Chun Su Hi Huang Chia-Hsien Lin Chen-Lee Huang Chi-Da Lin |
| Copyright Year | 1998 |
| Description | Author affiliation: Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan (Mu-Chun Su) |
| Abstract | Several successful approaches to spatio-temporal signal processing such as speech recognition and hand gesture recognition have been proposed. Most of them involve time alignment which requires substantial computation and considerable memory storage. In this paper, we present a neural-network-based approach to spatio-temporal pattern recognition. This approach employs a powerful method based on hyperrectangular composite neural networks (HRCNNs) for selecting templates, therefore, considerable memory is alleviated. In addition, it greatly reduces substantial computation in the matching process because it obviates time alignment. Two databases consisted of 51 spatio-temporal hand gestures were utilized for verifying its performance. An encouraging experimental result confirmed the effectiveness of the proposed method. |
| Starting Page | 2116 |
| Ending Page | 2121 |
| File Size | 654477 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780348591 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.1998.687186 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1998-05-04 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Intelligent networks Pattern recognition Speech recognition Pattern matching Signal processing Power engineering computing Databases Speech processing Recurrent neural networks |
| Content Type | Text |
| Resource Type | Article |
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