第３４卷第３期 ２０１１年３月 计 算 机 学 报 ＣＨＩＮＥＳＥ ＪＯＵＲＮＡＬ ＯＦ ＣＯＭＰＵＴＥＲＳ Ｖｏ１．３４ ＮＯ．３ Ｍａｒ．２Ｏ１１
一种面向大尺度变形的非刚性注册算法
李 俊 程志全 李宏华 陈 寅 姜 巍 党 岗 金士尧
（国防科学技术大学计算机学院并行与分布处理重点实验室长沙４１００７３）
摘 要 动态几何模型表示了随时间演变的动态对象，通过非刚性注册匹配离散的帧模型是重建动态几何模型的 核心问题．文中提出了一种鲁棒的成对非刚性注册算法，算法分为显式对应关系计算与全局变形优化两步：第１ 步，分析源模型和目标模型的滑动特征，提取显著特征点，建立特征点间的对应关系；第２步，利用显示的对应关 系，求解全局变形优化完成非刚性注册，通过最小化能量函数，实现源模型和目标模型的最优匹配．实验表明，对于 扫描获取和人工合成的数据，新算法突破了小尺度变形的限制，可以完成大尺度变形模型的成对注册．
关键词动态几何模型；非刚性注册；对应关系；全局变形优化；匹配 中图法分类号ＴＰ３９１ ＤＯＩ号：１０．３７２４／ＳＰ．Ｊ．１０１６．２０１１．００５３９
Ａ Ｎｏｎ・－Ｒｉｇｉｄ Ｒｅｇｉｓｔｒａｔｉｏｎ Ａｌｇｏｒｉｔｈｍ ｏｆ Ｌａｒｇｅ‘－Ｓｃａｌｅ Ｄｅｆｏｒｍａｂｌｅ Ｍｏｄｅｌｓ
ＬＩ Ｊｕｎ ＣＨＥＮＧ Ｚｈｉ—Ｑｕａｎ ＬＩ Ｈｏｎｇ—Ｈｕａ ＣＨＥＮ Ｙｉｎ ＪＩＡＮＧ Ｗｅｉ ＤＡＮＧ Ｇａｎｇ ＪＩＮ Ｓｈｉ—Ｙａｏ （Ｎａｔｉｏｎａｌ Ｌａｂｏｒａｔｏｒｙ ｆｏｒ Ｐａｒａｌｌｅｌ ａｎｄ Ｄｉｓｔｒｉｂｕｔｅｄ Ｐｒｏｃｅｓｓｉｎｇ，Ｓｃｈｏｏｌ ｏｆ Ｃｏｍｐｕｔｅｒ Ｓｃｉｅｎｃｅ， Ｎａｔｉｏｎａｌ Ｕｎｉｖｅｒｓｉｔｙ ｏｆ Ｄｅｆｅｎｓｅ Ｔｅｃｈｎｏｌｏｇｙ，Ｃｈａｎｇｓｈａ ４１００７３）
Ａｂｓｔｒａｃｔ Ｆｏｒ ｔｈｅ ｄｉｓｃｒｅｔｅ ｆｒａｍｅｓ，ｎｏｎ—ｒｉｇｉｄ ｒｅｇｉｓｔｒａｔｉｏｎ ｉｓ ｔｈｅ ｋｅｙ ｐｒｏｂｌｅｍ ｔｏ ｒｅｃｏｎｓｔｒｕｃｔ ｏｎｅ
辨识建模与仿真IdentificationModelingandSimulation《自动化技术与应用》2020年第39卷第3期
TechniquesofAutomation&Applications基于Kinect采集的武术动作识别匹配研究*周帅,张飞云,郑永权(西安交通大学城市学院，陕西西安710000)摘要:针对当前武术标准动作训练的需要，为提高武术训练的准确度，结合当前流行的三维图像采集技术，提出一种基于Kinect的动作识别技术。最后通过试验验证了基于Kinect在太极拳姿态采集方面的可行性，并且本文设计的姿态匹配方法，具有较高的识别率。关键词:Kinect采集；武术动作；姿态匹配；特征向量中图分类号：TP391.41文献标识码:A文章编号:1003-7241(2020)03-0094-04ResearchonWushuActionRecognitionandMatchingBasedonKinectAcquisitionZHOUShuai,ZHANGFei-yun,ZHENGYong-quan(Xi'anJiaotongUniversityCityCollege,Xi'an710000China)Abstract:InviewoftheneedsofthecurrentWushustandardactiontraining,inordertoimprovetheaccuracyofWushutraining,combinedwiththecurrentpopular3Dimageacquisitiontechnology,anactionrecognitiontechnologybasedonKinectisproposed.Finally,thefeasibilityoftheattitudeacquisitionofTaijiquanbasedonKinectisverifiedbytheexperiment,andtheattitudematchingmethoddesignedinthispaperhasahighrecognitionrate.Keywords:Kinectacquisition;Wushuaction;posturematching;featurevector
shape识人方法
英文回答：
Shape recognition is a method used to identify
individuals based on their physical appearance,
specifically the shape of their body or face. This
technique is often employed in various fields such as
biometrics, computer vision, and surveillance systems.
There are several ways to perform shape recognition, and I
will explain a few common methods.
One approach to shape recognition is using geometric
features. This involves analyzing the contours and
proportions of an individual's body or face to determine
their unique shape. For example, in facial shape
recognition, the distance between the eyes, the width of
the nose, and the length of the jawline can be measured and
compared to a database of known shapes. By matching these
geometric features, it is possible to identify a person.
人脸识别的英文文献15篇
英文回答：
1. Title: A Survey on Face Recognition Algorithms.
Abstract: Face recognition is a challenging task in
computer vision due to variations in illumination, pose,
expression, and occlusion. This survey provides a
comprehensive overview of the state-of-the-art face
recognition algorithms, including traditional methods like
Eigenfaces and Fisherfaces, and deep learning-based methods
such as Convolutional Neural Networks (CNNs).
2. Title: Face Recognition using Deep Learning: A
Literature Review.
Abstract: Deep learning has revolutionized the field
of face recognition, leading to significant improvements in
accuracy and robustness. This literature review presents an
in-depth analysis of various deep learning architectures and techniques used for face recognition, highlighting
