At last, optimization DTW algorithm is used to realize dynamic hand gesture recognition.
最后，采用优化的DT W算法实现了特定动态手势的识别。
Hand gesture recognition is composed of three parts, preprocessing hand gesture images, extracting image features and recognition.
手势识别的过程大致分为三个部分，手势图像预处理、手势图像特征提取和识别。
The processor can accelerate tasks like hand gesture recognition, face matching or face tracking, but is not designed to be a full-fledged CPU.
该处理器可以加速诸如手势识别、人脸匹配或表情跟踪，但并无意成为一个完整意义上的CPU。
The experiment result proves that hand gesture recognition algorithm based on key points of gesture image is very efficient. And the recognition ratio is proved 85% accuracy.
实验结果表明，基于手势边缘关键点特征的识别算法具有较好的识别效率，识别率近85%。
But currently, in the vision-based hand gesture recognition, almost all the technologies on hand gesture segmentation are based on simple background or on gloves in special colors.
但是目前基于单目视觉的手势识别技术中，手势分割要求背景简单或者要求识别者戴着笨重的 数据手套 。
Monocular hand gesture recognition systems usually model a human hand as a pixel or a blob by which the motion of the whole hand are analyzed and the appearance features are extracted.
单目视觉的手势识别系统，通常把人手建模成一个像素或者一块，从整体上分析手势的运动参数并提取表观特征。
Presents a hand tracking and gesture recognition algorithm for human-computer interaction with two web cameras.
为实现基于手势的智能人机交互，提出了一种基于视觉的人手跟踪与手势识别算法。
The technique system of intelligent sketching is presented, including the pen based natural gesture interface, free-hand sketching input, sketching recognition and sketch based solid modeling.
构建出智能草图的技术体系，包括基于单笔划的自然手势界面，徒手草图输入，草图识别与基于草图的几何建模。
Data glove is a generally applicable method for gesture recognition, and virtual hand technology is provided as a natural and efficient method of human-computer interaction.
数据手套是目前广泛使用的手势输入方式，基于数据手套的虚拟手技术可以提供自然高效的人机交互方式。
Most gesture recognition systems use skin color model to segment hand. The segmentation results are affected by to the light, background and other conditions.
目前手势识别系统主要是利用肤色模型分割手部区域，检测结果易受光照、背景等条件影响。
Gesture recognition of human hand based on data glove and motion mapping from human hand to virtual hand are studied.
研究了基于数据手套的人手手势的获取和人手到虚拟手的动作映射方法。
For gesture images in YUV field, firstly we divide hand image from complex background, then make further process, then make recognition by using neural network.
对手部图像，在YUV域中，首先将待识别图像从复杂背景中划分出来，这样再进一步处理，然后利用神经网络法进行识别。
For result verification we have defined 8 types of hand gesture, the experiment is conducted with 7 participants aiming at 5 scenarios. The average recognition rate is 94.1%.
结果验证部份，我们定义八种手势，分别由七位测试者针对五种状况测试，整体平均辨识率为94.1%。
For result verification we have defined 8 types of hand gesture, the experiment is conducted with 7 participants aiming at 5 scenarios. The average recognition rate is 94.1%.
结果验证部份，我们定义八种手势，分别由七位测试者针对五种状况测试，整体平均辨识率为94.1%。
应用推荐