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基于改进凸分解的手势识别

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基于改进凸分解的手势识别
Hand Gesture Recognition Based on an Improved Convex Shape Decomposition
投稿时间：2016-07-16
DOI：10.15918/j.tbit1001-0645.2018.03.010
中文关键词:手势识别凸分解骨架图匹配深度信息
English Keywords:gesture recognitionconvex shape decompositiongesture skeletonskeleton graph matchingdepth information
基金项目:国家自然科学基金重点资助项目（61332017）；国家科技支撑计划项目（2015BAK04B05）；国家重点研发计划资助项目（2017YFB1002803）
作者单位E-mail
潘志庚杭州师范大学 数字媒体与人机交互研究中心, 浙江, 杭州 311121
王顺婷杭州师范大学 数字媒体与人机交互研究中心, 浙江, 杭州 311121
姚争为杭州师范大学 数字媒体与人机交互研究中心, 浙江, 杭州 311121iron-yao@qq.com
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中文摘要:
提出了一种新的基于Kinect的实时静态手势识别方法，主要贡献包括：提出了一种简易可行的、结合图像深度信息与肤色信息的手势区域检测与分割方法；提出了一种改进的凸分解算法，对手势区域进行近似凸形状分解，以得到表征手势特征的骨架信息；采用基于路径相似性的骨架图匹配算法对手势进行匹配以实现识别.针对特定手势集进行了对比实验，实验结果表明，本文方法在识别结果的准确率以及算法的效率上都有着良好的表现.
English Summary:
Hand gesture recognition has become one of the most natural way of human communication with computer, However, the recognition accuracy and efficiency of the traditional methods still need to be improved. In this paper, a novel method was proposed based on Kinect for static gesture recognition in real time. Firstly, a simple and feasible hand detection and segmentation method was put forward based on depth information and skin color information. Then, a improved convex shape decomposition method was developed to obtain hand skeleton, and a gesture recognition method was put forward based on comparing the geodesic paths between skeleton endpoints. Finally, a comparative experiment was carried out for a specific gesture set. The experimental results show that, this method can provided a good performance in recognition accuracy and efficiency of the algorithm.
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