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, therefor, 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.
|Number of pages||6|
|State||Published - 1998|
|Event||Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA|
Duration: 4 May 1998 → 9 May 1998
|Conference||Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)|
|City||Anchorage, AK, USA|
|Period||4/05/98 → 9/05/98|