A TRAINABLE GESTURE RECOGNIZER

被引:12
作者
LIPSCOMB, JS
机构
[1] IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598
关键词
CROSS PRODUCT; VECTOR PRODUCT; GESTURE RECOGNITION; MULTISCALE RECOGNITION; ONLINE RECOGNITION; REAL-TIME RECOGNITION;
D O I
10.1016/0031-3203(91)90009-T
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Gestures are hand-drawn strokes that do things. These things happen at distinctive places on the stroke. We built a gesture input filter and recognizer. The input filter is fast, because it does few computations per input point, because it can omit pre-filter data smoothing, and because wild points caused by hardware glitches are removed at the few output points of the filter, not at the many input points. The recognizer is a novel combination of two traditional techniques; angle filtering and multi-scale recognition. Because an angle filter does not produce well-behaved scaled output, the multi-scale treatment had to be unusual.
引用
收藏
页码:895 / 907
页数:13
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