基于CLCS特征提取的动态手势预测系统

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI:10.1109/BRICS-CCI-CBIC.2013.89

Nestor T. M. Junior, Pablo V. A. Barros, Bruno José Torres Fernandes, B. Bezerra, Sergio M. M. Fernandes

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引用次数: 1

摘要

动态手势的实时识别是目前大多数应用的难题。预测方法可以作为一种解决方案。这种方法使用不完整的手势输入，并尝试预测给定输入代表的手势。本文提出了一种动态手势特征提取技术——凸局部轮廓序列(CLCS)作为预测任务的提取器。本文使用了两种预测系统来实现这一任务，并对结果进行了比较和讨论。

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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A Dynamic Gesture Prediction System Based on the CLCS Feature Extraction

Real-time recognition of dynamic gestures is a problem for most of the applications nowadays. The prediction approach can be used as a solution for this. This approach uses an incomplete gesture input and it tries to predict which gesture the given input represents. This paper presents the application of the dynamic gesture feature extraction technique called Convexity Local Contour Sequence (CLCS) as the extractor for the prediction task. Two predictor systems are used to achieve this task and results are compared and discussed in this paper.

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2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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