基于标准模糊c均值算法的无监督手势识别系统

2008 First International Conference on Emerging Trends in Engineering and Technology Pub Date : 2008-07-16 DOI:10.1109/ICETET.2008.90

Sachin K. Korde, K. Jondhale

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引用次数: 12

摘要

本文描述了一种识别手势指令的手势识别系统。使用模糊c均值聚类方法将手势分类为“手势”。对模糊识别系统进行了用户依赖型和用户独立型手势词汇的测试。结果表明，识别率(用户独立手势与用户依赖手势的比值)识别正确率为100%。用户自主手势识别率为54%。没有任何手势被错误识别。执行推推任务的性能时间显示出快速学习，由一个没有经验的操作员在4到6次试验中达到标准时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Hand Gesture Recognition System Using Standard Fuzzy C-Means Algorithm for Recognizing Hand Gesture with Angle Variations for Unsupervised Users

This paper describes a gesture recognition system in which a hand gesture commands are recognized. A fuzzy C-means clustering method is used to classify hand postures as "gestures". The fuzzy recognition system was tested for both user dependent and user independent gestures vocabulary. Results revealed recognition rate (the ratio of user independent gestures to user dependent gestures) recognition accuracy the percent of he user dependent gestures recognized correctly of 100%. And user independent gestures recognized correctly of 54%. No gestures was recognized incorrectly. Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.

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2008 First International Conference on Emerging Trends in Engineering and Technology

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