Light invariant real-time robust hand gesture recognition
Light invariant real-time robust hand gesture recognition
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DOI：
10.1016/j.ijleo.2017.11.158
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发表时间：
2018-01-01
期刊：
影响因子：
3.1
通讯作者：
Raheja, J. L.
中科院分区：
文献类型：
--
作者：
Chaudhary, Ankit;Raheja, J. L.
计算机视觉已经扩展到不同的领域，以促进困难的操作。它作为人工眼用于许多工业应用，以观察元件，过程，自动化和发现缺陷。基于视觉的系统也可以应用于正常的人类生活操作，但改变光线条件是这些系统的一个大问题。手势识别可以嵌入许多现有的交互式应用程序/游戏，使交互自然和容易，但不断变化的照明和不均匀的背景，使它很难执行良好的图像分割操作。如果一个基于视觉的系统被安装在公共领域，不同的人应该在应用程序上工作.本文演示了一个光强不变的手势识别技术，它可以很容易地应用到其他基于视觉的应用程序也。这项技术已经在不同的人身上进行了测试，在不同的光线条件下，强度发生了极大的变化。这是因为一种皮肤颜色在改变的光强度下看起来不同，而不同的皮肤颜色在改变的光强度下看起来可能相同。方向直方图被用来识别手势的独特特征，并使用监督人工神经网络进行比较。在极端光照强度变化的环境中，实现了92.86%的整体准确率。(C)2017 Elsevier GmbH. All rights reserved.
Computer vision has spread over different domains to facilitate difficult operations. It works as the artificial eye for many industrial applications to observe elements, process, automation and to find defects. Vision-based systems can also be applied to normal human life operations but changing light conditions is a big problem for these systems. Hand gesture recognition can be embedded with many existing interactive applications/games to make interaction natural and easy but changing illumination and non-uniform backgrounds make it very difficult to perform operations with good image segmentation. If a vision based system is installed in public domain, different people are supposed to work on the application.This paper demonstrates a light intensity invariant technique for hand gesture recognition which can be easily applied to other vision-based applications also. The technique has been tested on different people in different light conditions with the extreme change in intensity. This was done as one skin color looks different in changed light intensity and different skin colors may look same in changed light intensity. The orientation histogram was used to identify unique features of a hand gesture and it was compared using supervised ANN. The overall accuracy of 92.86% is achieved in extreme light intensity changing environments. (C) 2017 Elsevier GmbH. All rights reserved.