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基于几何矩的手势识别算法

【作者】 殷涛

【导师】 葛元;

【作者基本信息】 上海海事大学 , 计算机应用技术, 2004, 硕士

【摘要】 手势识别的研究具有广阔的实际应用前景,具体表现在诸多方面如:对语音识别起着辅助作用;利用手势控制VR(Virtual Reality)中的智能化;机器人的示范学习;虚拟现实系统中的多模式接口;可以使聋哑人使用手语和正常人交流等。另外,手语的研究涉及到教学、计算机图形学、机器人运动学、医学等多学科。 本文结合上海市自然科学基金资助课题“手势识别”,从手势图像的预处理、手势特征提取和手势识别等三个方面研究了基于视觉的手势识别的识别算法。提出了一种基于统计分析的手势识别的方法,利用几何矩进行特征提取,并应用到手势识别中去,解决了手势识别过程中手势的旋转,缩放,尺度变换所带来的问题,使得手势识别系统具有很好的稳定性。 在图像预处理阶段,我们主要是对手势字母图像进行平滑、锐化和二值化的处理。平滑主要采用模板操作;锐化过程中使用拉普拉斯算子进行锐化:而二值化则采用的是最大化方差的做法。 特征提取的好坏则直接关系到手势识别的效果。由于手势图像具有旋转、尺度等不确定性,给特征提取带来了诸多困难,而几何矩是一种基于统计分析的算法,本文即将其应用于提取不同手势的特征,被提取的特征可以做到不随图像的旋转、平移的变化而变化,应用到手势识别系统中去表现出有着良好的适应性、稳定性。 在样本库的建立过程中,我们采集了中国手语字母手势,共三套,其中两套作学习用,以形成标准库,第三套则用来测试算法的识别率。 识别过程则是将输入的手势图像进行一些预处理、提取特征,再与标准库中的手势的特征做比对,距离(加权的欧几里德距离)最小的即为匹配手语。 实验中,我们使用了汉语手势字母的全部30个手势,并加入阿拉伯数字10个手势,共40个手势,实验中一次识别率达到86.7%,累积二次识别率即可达93%。

【Abstract】 The Research of Gesture-Language can be applied in many fields such as Computer Access Gesture-Language Teaching, TV Bilingual Broadcasting, the search of Virtual Human. The search of Gesture-Language includes the following subjects : Teaching, Computer Graphology, Robot Motion and Physic etc. It is a very meaningful subject. The Search of Gesture Recognition has a wide range of applications such as : the communication between the deaf and the normal, the access recognition of voice recognition ,the control of VR, the study of robot.This article gives a method of Gesture Recognition based on Statistics. We can apply the Geometric Moment in the Feature Extraction. We can solve the problem of Recognition brought by rotation and scale.The Gesture Recognition mainly includes the following process: Image preprocessing, feature extraction, pattern recognition.In the process of image preprocessing there are several image operations such as image smooth, image sharpen and image segment. We use the template operation in the process of image smoothing, laplacian operator hi the process of image sharpening and maximum variance method in the process of Region segment.Feature Extraction is vital to Gesture Recognition. The uncertainty of rotation, scale of gesture brings many difficulties to the extraction of feature. Geometric Moment is a arithmetic based on statistics. This article applys the arithmetic in the extraction of gesture feature. The feature can remains the same when the image rotating and scaling.In the process of constructing the sample lib, we collected three sets of Chinese Letter Gesture. Two sets were used to machine learning, the third was used to test the recognition rate the Gesture Recognition System.The Recognition Process includes the following phases: firstly, image preprocessing; secondly , feature extraction ;finally, matching with the Standard Gesture Lib. The Gesture which has the minimum Distance to the Input Gesture is theRecognition Result-In the experiment, I used 30 Chinese Pinyin gestures and 10 Arabic number gestures, the cumulate recognition rate is up to 93 %.Tao YinDirected by -.Yuan Ge

  • 【分类号】TP391.4
  • 【被引频次】21
  • 【下载频次】760
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