> Problem statement: With the development of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient. Due to the limitation of these devices the useable command set is also limited. Direct use of hands can be used as an input device for providing natural interaction. Approach: In this study, Gaussian Mixture Model (GMM) was used to extract hand from the video sequence. Extreme points were extracted from the segmented hand using star skeletonization and recognition was performed by distance signature. Results: The proposed method was tested on the dataset captured in the closed environment with the assumption that the user should be in the Field Of View (FOV). This study was performed for 5 different datasets in varying lighting conditions. Conclusion: This study specifically proposed a real time vision system for hand gesture based computer interaction to control an event like navigation of slides in Power Point Presentation.
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机译： > 问题陈述：随着无处不在的计算的发展，当前的用户与键盘，鼠标和笔的交互方法还远远不够。由于这些设备的限制，可用命令集也受到限制。直接使用手可用作提供自然互动的输入设备。 方法：在这项研究中，使用了高斯混合模型（GMM）从视频序列中提取手。使用星形骨架从分割的手中提取极端点，并通过距离签名进行识别。 结果：假设用户应该在视场（FOV）中，并在封闭环境中捕获的数据集上对提出的方法进行了测试。这项研究是针对5个不同的数据集在不同的光照条件下进行的。结论：该研究专门针对基于手势的计算机交互提供了一种实时视觉系统，以控制诸如Power Point Presentation中的幻灯片导航之类的事件。
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