Gesture Spotting by Using Vector Distance of Self-organizing Map

被引：0

作者：

Ichikawa, Yuta ^{[1
]}

Tashiro, Shuji ^{[1
]}

Ito, Hidetaka ^{[1
]}

Hikawa, Hiroomi ^{[1
]}

机构：

[1] Kansai Univ, Dept Elect & Elect Engn, Suita, Osaka, Japan

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II | 2016年 / 9948卷

关键词：

Self organizing map (SOM); Hebb learning; Hand sign recognition; Gesture spotting; RECOGNITION; SYSTEM;

D O I：

10.1007/978-3-319-46672-9_47

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a dynamic hand gesture recognition algorithm with a function of gesture spotting. The algorithm consists of two self-organizing maps (SOMs) and a Hebb learning network. Feature vectors are extracted from input images, and these are fed to one of the SOMs and a vector that represents the sequence of postures in the given frame is generated. Using this vector, gesture classification is performed using another SOM. In the SOM, the vector distance between the input vector and the winner neuron's weight vector is used for the gesture spotting. The following Hebb network identifies the gesture class. The experimental results show that the system recognizes eight gestures with the accuracy of 95.8%.

引用

页码：419 / 426

页数：8

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