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清华大学2. 四川工程职业技术学院
收稿日期:2011-03-17,
修回日期:2011-04-18,
网络出版日期:2011-10-21,
纸质出版日期:2011-08-25
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向守兵,苏光大,任小龙,吉倩倩,方飞. 实时手指交互系统的嵌入式实现[J]. 光学精密工程, 2011, 19(8): 0-0.
XIANG Shou-Bing,SU Guang-Tai,REN Xiao-Long,JI Qian-Qian,FANG Fei. The embedded implementation of real-time finger interaction system[J]. Editorial Office of Optics and Precision Engineering, 2011, 19(8): 0-0.
为满足便携式设备中人机交互的需要,设计了嵌入式手指交互系统。对系统所采用的肤色分割、凸包计算、指尖检测等算法进行研究
并完成硬件设计。首先,根据肤色的聚类特征,在对比分析常用彩色空间特性的基础上建立肤色模型,对人手进行分割。接着,提出射线扫描法对经典Graham扫描算法进行改进,快速计算人手凸点。然后,分析了利用手指轮廓弯曲特征检测指尖的算法。最后,介绍了以DSP和FPGA为微处理器构成的硬件系统。实验结果表明:对自然伸展的单个手指正确检测率为95.2%;对弯曲手指的正确检测率为92.6%;对在非目标手指干扰下的正确检测率为90.1%;对指尖的定位最大偏移量为2.12mm;指尖定位总耗时约为23ms。所设计的嵌入式手指交互系统稳定可靠、自然、友好
满足实时要求。
In order to meet the need of human-machine interaction of the portable equipments
an embedded finger-interaction system is implemented. Its applied algorithms such as skin color segmentation
convex hull computation
fingertip detection and etc have been investigated and the hardware has been designed. Firstly
according to the clustering character of skin color
the human hand is segmented via building the skin color model by analyzing the characters of common color spaces. Secondly
the classic Graham-scan method is improved based on the method of radial-scan
and the convex hull of human hands can be computed quickly. Thirdly
the algorithm of fingertip detection which uses the curve of human finger is discussed. Finally
the DSP and FPGA based hardware structure is introduced. Experimental results indicate that the detecting precision is 95.2% for a natural stretched finger
is 92.6% for the bended finger and is 90.1% under the disturbance of other fingers. The maximum offset of fingertip location is 2.12mm and the location time is 23ms. The system is nature and friendly
has strong stabilization and can satisfy the real-time requirement.
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