运动捕捉数据智能处理算法研究及应用
|
摘要
运动捕捉技术是获取运动数据的一种重要手段,它是计算机动画、数学、计算机图形学、光学等多学科交叉的一项新兴技术。由于其功能强大,近年来吸引了众多国内外学者的研究兴趣。运动捕捉技术不但具有重要的理论研究意义,而且具有非常广泛的实际应用价值,目前,运动捕捉已广泛应用于动画制作、影视特效、游戏制作、医学分析、体育训练以及航天国防等领域。
随着运动捕捉技术的发展和广泛应用,一些与运动捕捉相关的问题也相继被提出,国内外学者对这些问题进行了深入的研究与分析,并取得大量非常有价值的研究成果,不过目前仍然存在一些关键问题尚未得到很好地解决。本文针对运动捕捉技术中数据处理及其应用方面的一些关键问题进行了研究,并取得一些阶段性成果,主要研究内容包括以下几个方面：
1)被动式光学人体运动捕捉散乱数据处理：针对运动捕捉原始数据中噪声点问题、散乱数据识别匹配问题以及缺失数据的修正问题提出了一种基于模板匹配的人体运动捕捉数据处理的方法。在噪声数据处理方面,采用聚类合并的方法,将以点簇形式存在的噪声点合并为一个有效点；在散乱数据匹配方面,将一帧已处理数据作为模板,利用人体刚性拓扑结构形状匹配完成首帧数据的初始化,采用线性预测跟踪与形状匹配相结合的方法对后续散乱数据进行识别跟踪；在缺失点处理方面,提出了关于两种不同情况下的缺失点重构算法,对于包含4个以上关节点的具有刚性结构的子模板,采用基于特征关联的缺失数据修补方法,根据相邻数据对缺失数据进行修正；对于膝关节,其关联信息不足以对缺失数据进行修补,于是提出一种局部线性预测的缺失数据修补方法,综合考虑膝关节与踝关节的关联性以及它们的运动趋势对缺失数据进行修补。最后通过实验对本文方法的有效性以及处理效率进行了验证。
2)人体运动捕捉数据检索与分割技术研究：分别提出基于最小二乘距离的相似人体运动姿态检索方法和基于最小二乘距离特征曲线的人体运动序列分割方法。在人体运动姿态相似性度量方面引入最小二乘距离的概念,对不同人体运动姿态相似性度量进行了定义,设计并实现了一种相似姿态的快速检索方法,以满足从大规模数据库中进行相似检索的要求。在以上最小二乘距离概念的基础之上,将人体运动序列中每个姿态与模板之间的最小二乘距离作为人体运动序列特征,从而将人体运动序列简化为一条特征曲线,通过对人体运动层次化结构分析、局部极值点提取、动素相似性聚类分析等步骤最终实现人体运动序列在语义层次上的运动序列分割。
3)基于运动捕捉的空间机器人运动控制研究：针对支持在轨模块更换的空间机器人运动仿真问题,提出了一种基于运动捕捉数据的空间机器人运动控制方法。通过对机械臂运动的捕捉建立机械臂动作数据库,根据模块更换任务的需求,提出基于语义的空间机器人运动检索方法和机械臂运动序列重构方法,在已构建的机械臂动作库中检索并合成满足任务需求的机械臂运动序列；然后通过对运动序列进行约束编辑、运动数据匹配以及平滑滤波等算法得到可以驱动机械臂运动的数据；最后将本文方法与支持在轨模块更换的空间机器人运动仿真系统相融合,实现了空间机器人的运动驱动。
综上所述,本文围绕以上三个方面的研究内容,提出相应的算法及其实现方法,针对算法建立了相应仿真实验平台,并对以上方法的有效性进行了实验验证,为实现相关软件及系统的开发提供了算法基础和理论依据。
The technique of motion capture (Mocap) is an important approach for motion data acquisition, and it is a new rising technique in the intersection of multi-disciplinary such as computer animation, maths, computer graphics, optics and so on. As its powerful function and important application value, more and more researchers have focused their interest on motion capture. The motion capture technique not only has meaningful research value, and it is very useful also. Now this technique has been widely used in computer animation, movie making,3D game, medical analysis, physical training and so on.
With the development of the motion capture, some problems encountered in the utilizing of motion data have been proposed. Many research works have been done for these problems by the researchers abroad and at home, and lots of fruitful achievements are obtained. But there are still some open problems which have not been resolved. Aims at the key problems in motion capture data processing and application, some relative work has been done in this thesis, the main work can be concluded as follows:
1) Scattered data processing for passive optical motion capture data:in the passive optical motion capture system, there are three important research topics, they are noise data processing, scattered data matching and missing data fitting. Aim at these problems, an approach based on template matching is proposed for the processing of motion capture data. In noise data processing, a clustering method is used which could combine the noise data in a points cluster into one available point. In the scattered data matching, the human template is segmented to several rigid sub-templates by the local rigid structure. Matching point-set with least error is searched for each sub-template from the human motion capture data by affine transformation, and the scattered data is identified by point tracking. The ambiguity of identification is notability reduced. In the missing data processing, two different missing data fitting method are proposed corresponding to the different situation, if a sub-template contains more than four markers, the missing data can be fitted by the relationship of its neighbors; but for the knee joint point, there only two markers in a rigid structure, in this situation we use the local linear prediction method to fit the missing data. At last, in order to proof the validity of the data processing method, some experiments are carried out, and the experiment results are satisfactory.
2) Data reusage techniques for human motion capture:the feature representation of human motion capture data based on least-square (L-S) distance is proposed. Based on the feature representation a similar pose retrieval method and a motion sequence segmentation method are designed. The similarity measurement of different human motion poses is defined by the least-square distance, and a similar pose retrieval method is designed which can be used to search similar poses from huge motion data base quickly. Under the feature representation, the human motion sequence can be simplified into a one dimension feature curve, and the motion sequence is segment into some motion units. At last, we can segment the motion sequence into some semantic primitive motion clips by the motion units clustering and hierarchy structure analysis of human motion.
3) Robot control based on motion capture technique:a semantic based motion retrieval method if proposed for the simulation of space robot which supports the on-orbit module replacement. First, the motion data of space robot is acquired by Mocap equipment, and the data base is constructed by the Mocap data. Then, the least-square distance of two3D points' sets is used to define the similarity between two motion poses of the space robot, and a motion retrieval method is designed by the similarity measurement. At last, we reconstructed the Mocap data and use the reconstructed data to drive the model of space robot. In order to prove the efficiency of the method proposed in this paper, some experiments is carry out, the experiment results indicate that the motion sequences got by the retrieval method is smooth and consecutive and which demonstrate the efficiency of the method proposed in this paper.
To sum up, this thesis presents the corresponding algorithms and implements methods surrounding about the main research content. The corresponding experiment platforms are constructed and the validity of the algorithms is examined. All the study will provide the theoretical basis and algorithmic rule for the design and development of software system.
随着运动捕捉技术的发展和广泛应用,一些与运动捕捉相关的问题也相继被提出,国内外学者对这些问题进行了深入的研究与分析,并取得大量非常有价值的研究成果,不过目前仍然存在一些关键问题尚未得到很好地解决。本文针对运动捕捉技术中数据处理及其应用方面的一些关键问题进行了研究,并取得一些阶段性成果,主要研究内容包括以下几个方面：
1)被动式光学人体运动捕捉散乱数据处理：针对运动捕捉原始数据中噪声点问题、散乱数据识别匹配问题以及缺失数据的修正问题提出了一种基于模板匹配的人体运动捕捉数据处理的方法。在噪声数据处理方面,采用聚类合并的方法,将以点簇形式存在的噪声点合并为一个有效点；在散乱数据匹配方面,将一帧已处理数据作为模板,利用人体刚性拓扑结构形状匹配完成首帧数据的初始化,采用线性预测跟踪与形状匹配相结合的方法对后续散乱数据进行识别跟踪；在缺失点处理方面,提出了关于两种不同情况下的缺失点重构算法,对于包含4个以上关节点的具有刚性结构的子模板,采用基于特征关联的缺失数据修补方法,根据相邻数据对缺失数据进行修正；对于膝关节,其关联信息不足以对缺失数据进行修补,于是提出一种局部线性预测的缺失数据修补方法,综合考虑膝关节与踝关节的关联性以及它们的运动趋势对缺失数据进行修补。最后通过实验对本文方法的有效性以及处理效率进行了验证。
2)人体运动捕捉数据检索与分割技术研究：分别提出基于最小二乘距离的相似人体运动姿态检索方法和基于最小二乘距离特征曲线的人体运动序列分割方法。在人体运动姿态相似性度量方面引入最小二乘距离的概念,对不同人体运动姿态相似性度量进行了定义,设计并实现了一种相似姿态的快速检索方法,以满足从大规模数据库中进行相似检索的要求。在以上最小二乘距离概念的基础之上,将人体运动序列中每个姿态与模板之间的最小二乘距离作为人体运动序列特征,从而将人体运动序列简化为一条特征曲线,通过对人体运动层次化结构分析、局部极值点提取、动素相似性聚类分析等步骤最终实现人体运动序列在语义层次上的运动序列分割。
3)基于运动捕捉的空间机器人运动控制研究：针对支持在轨模块更换的空间机器人运动仿真问题,提出了一种基于运动捕捉数据的空间机器人运动控制方法。通过对机械臂运动的捕捉建立机械臂动作数据库,根据模块更换任务的需求,提出基于语义的空间机器人运动检索方法和机械臂运动序列重构方法,在已构建的机械臂动作库中检索并合成满足任务需求的机械臂运动序列；然后通过对运动序列进行约束编辑、运动数据匹配以及平滑滤波等算法得到可以驱动机械臂运动的数据；最后将本文方法与支持在轨模块更换的空间机器人运动仿真系统相融合,实现了空间机器人的运动驱动。
综上所述,本文围绕以上三个方面的研究内容,提出相应的算法及其实现方法,针对算法建立了相应仿真实验平台,并对以上方法的有效性进行了实验验证,为实现相关软件及系统的开发提供了算法基础和理论依据。
The technique of motion capture (Mocap) is an important approach for motion data acquisition, and it is a new rising technique in the intersection of multi-disciplinary such as computer animation, maths, computer graphics, optics and so on. As its powerful function and important application value, more and more researchers have focused their interest on motion capture. The motion capture technique not only has meaningful research value, and it is very useful also. Now this technique has been widely used in computer animation, movie making,3D game, medical analysis, physical training and so on.
With the development of the motion capture, some problems encountered in the utilizing of motion data have been proposed. Many research works have been done for these problems by the researchers abroad and at home, and lots of fruitful achievements are obtained. But there are still some open problems which have not been resolved. Aims at the key problems in motion capture data processing and application, some relative work has been done in this thesis, the main work can be concluded as follows:
1) Scattered data processing for passive optical motion capture data:in the passive optical motion capture system, there are three important research topics, they are noise data processing, scattered data matching and missing data fitting. Aim at these problems, an approach based on template matching is proposed for the processing of motion capture data. In noise data processing, a clustering method is used which could combine the noise data in a points cluster into one available point. In the scattered data matching, the human template is segmented to several rigid sub-templates by the local rigid structure. Matching point-set with least error is searched for each sub-template from the human motion capture data by affine transformation, and the scattered data is identified by point tracking. The ambiguity of identification is notability reduced. In the missing data processing, two different missing data fitting method are proposed corresponding to the different situation, if a sub-template contains more than four markers, the missing data can be fitted by the relationship of its neighbors; but for the knee joint point, there only two markers in a rigid structure, in this situation we use the local linear prediction method to fit the missing data. At last, in order to proof the validity of the data processing method, some experiments are carried out, and the experiment results are satisfactory.
2) Data reusage techniques for human motion capture:the feature representation of human motion capture data based on least-square (L-S) distance is proposed. Based on the feature representation a similar pose retrieval method and a motion sequence segmentation method are designed. The similarity measurement of different human motion poses is defined by the least-square distance, and a similar pose retrieval method is designed which can be used to search similar poses from huge motion data base quickly. Under the feature representation, the human motion sequence can be simplified into a one dimension feature curve, and the motion sequence is segment into some motion units. At last, we can segment the motion sequence into some semantic primitive motion clips by the motion units clustering and hierarchy structure analysis of human motion.
3) Robot control based on motion capture technique:a semantic based motion retrieval method if proposed for the simulation of space robot which supports the on-orbit module replacement. First, the motion data of space robot is acquired by Mocap equipment, and the data base is constructed by the Mocap data. Then, the least-square distance of two3D points' sets is used to define the similarity between two motion poses of the space robot, and a motion retrieval method is designed by the similarity measurement. At last, we reconstructed the Mocap data and use the reconstructed data to drive the model of space robot. In order to prove the efficiency of the method proposed in this paper, some experiments is carry out, the experiment results indicate that the motion sequences got by the retrieval method is smooth and consecutive and which demonstrate the efficiency of the method proposed in this paper.
To sum up, this thesis presents the corresponding algorithms and implements methods surrounding about the main research content. The corresponding experiment platforms are constructed and the validity of the algorithms is examined. All the study will provide the theoretical basis and algorithmic rule for the design and development of software system.
引文
[1]姚敏.中国动漫面临历史机遇[N].中国消费者报,2010.
[2]蔡琪,郭静博.动漫产业发展及其计算机技术探讨[J].信息与电脑,2011,(09)：15-17.
[3]2007-2008年世界动漫产业发展研究年度总报告.http://market.ccidnet.com/
[4]“十二五”规划助力动漫产业春天正在到来.http://www.ce.cn/
[5]Sturman DJ. A brief history of motion capture for computer character animation[J]. SIGGRAPH94, Course9,1994.
[6]Calvert TW, Chapman J, Patla A. Aspects of the kinematic simulation of human movement[J]. IEEE Computer Graphics and Applications,1982,2(9):41-50.
[7]Ginsberg CM, Maxwell D. Graphical marionette[C]. In:Proc. ACM SIGGRAPH/SIGART Workshop on Motion,1983:172-179.
[8]Robertson B. Mike, the talking head[J]. Computer Graphics World,1989,11(7): 15-17.
[9]Walters G. The story of Waldo C. Graphic[C]. SIGGRAPH' 89. ACM Press,1989.65-79.
[10]Walters G. Performance animation at PDI [C]. SIGGRAPH'93. ACM Press,1993.40-53.
[11]Kleiser J. Character motion systems[C]. SIGGRAPH'93. ACM Press,1993.33-36.
[12]Tardif H. Character animation in real time[C]. SIGGRAPH Panel Proceedings, I: Reports from the Field,1991.
[13]Robertson B. Moving pictures[J]. Computer Graphics World,1992,15(10):38-44.
[14]Sang Il Park, Jessica K. Hodgins. Capturing and Animating Skin Deformation in Human Mot ion[J]. ACM Transaction on Graphics,2006,25(3):881-889.
[15]HSIEH CC., KUO PL. An impulsive noise reduction agent for rigid bodymotion data usingB-spline wavelets[J]. Expert Systems with Applications,2008,34(3): 1733-1741.
[16]Alonso FJ, Del Castillo JM, Pintado P. Motion data processing and wobbling mass modelling in the inverse dynamics of skeletal models[J]. Mechanism and Machine Theory,2007,42(9):1153-1169.
[17]高申玉,刘金刚.人体运动实时捕捉设备传输数据的滤波与处理[J].计算机工程与设计,2006,27(15)：2715-2717.
[18]Aissaoui R, Husse S, Mecheri H, et al. Automatic filtering techniques for three-dimensional kinematics data using 3D motion capture system[C]. Proceeding of IEEE International Symposium on Industrial Electronics,2006:614-619.
[19]夏时洪,魏毅,王兆其.一种对人体运动捕获数据中的噪声和误差进行修正的方法[P].中国,专利号：200610113071.8,2006.
[20]Adam G.Kirk, James F.O Brien, David A. Forsyth. Skeletal Parameter Estimation from Optical Motion Capture Data[C]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05),2005:782-788.
[21]Maurice Ringer, Joan Lasenby. Multiple Hypothesis Tracking for Automatic Optical Motion Capture[C]. Computer Vision — ECCV 2002. Springer,2002.524-536.
[22]Herda L, Fua P, Plankers R, et al. Skeleton-based motion capture for robust reconstruction of human motion[C]. Proceedings of Computer Animation 2000,2000: 77-83.
[23]汪亚明,楼正国,卞昕,et a1.一种非刚体运动图像序列的特征点对应方法[J].中国图象图形学报,2000,5(3)：232-236.
[24]Antonio CS, Luis EL, Jose RFB, et al. A motion capture system using passive markers[C]. Proceedings of the 2004 ACM SIGGRAPH international conference,2004: 440-447.
[25]黄波十,陈福民,张金剑.基于序列帧的光学运动捕捉系统及实现[J].计算机工程与应用,2004,40(17)：36-39.
[26]黄波十,陈福民，张金剑.一种改进算法的光学运动捕捉系统[J].同济大学学报(自然科学版),2005,33(10)：1372-1376.
[27]Kurihara K, Hoshino S, Yamane K, et al. Optical mot ion capture system with pan-tilt camera tracking and real time data processing[C].2002 IEEE, International Conference on Robotics & Automation,2002:1241-1248.
[28]Horst Holstein, Baihua Li. Low Density Feature Point Matching for Articulated Pose Identification[C]. British Machine Vision Conference.2002.678-687.
[29]Li BH, Meng Q, Horst Holstein. Reconstruction of segmentally articulated structure in freeform movement with low density feature points[J]. Image and Vision Computing 2004,22749-759.
[30]Li BH, Meng Q, Horst Holstein. Articulated motion reconstruction from feature points[J]. Pattern Recognition Letters,2008,41418-431.
[31]肖伯祥,魏小鹏,张强,et a1.光学运动捕捉散乱数据处理的一种方法[J].系统仿真学报,2008,20(2)：382-385.
[32]Herda L, Fua P, Plankers R, et al. Skeleton-based motion capture for robust reconstruction of human motion[C]. Proceeding of Computer Animation, Washington DC,2000:77-83.
[33]Hornung A, Sar-Dessai S, Kobbelt L. Self-calibrating optical motion tracking for articulated bodies[C]. Proceeding of IEEE Virtual Reality Conference,2005: 75-82.
[34]Koh S, Kim C. Geometrical fitting of missing data for shape from motion under noise distribution[J]. Advances in Natural Computation,2006,774-783.
[35]Liu G, Zhang J, Wang W, et al. Human motion estimation from a reduced marker set [C]. Proc ofACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, California, 2006:35-42.
[36]Liu G, McMillan L. Estimation of missing markers in human motion capture [J]. The Visual Computer,2006,22(9):721-728.
[37]Zhuang YT, Pan YH, Xiao J.Intelligent Techniques for Processing and Management of Motion Data. A Modern Approach to Intelligent Animation, Springer Berlin Heidelberg,2008.
[38]刘丰,庄越挺,吴飞,et a1.基于例子的三维运动检索[J].计算机辅助设计与图形学学报,2003,15(10)：1275-1280.
[39]Liu F, Zhuang YT, Wu F.3D motion retrieval with motion index tree[J]. Computer Vision and Image Understanding,2003,92(2):265-284.
[40]Lin Y. Efficient Motion Search in Large Motion Capture Databases[C]. Second International Symposium on Visual Computing,2006:151-160.
[41]Pradhan GN, Li CJ, Balakrishnan Prabhakaran. Hierarchical Indexing Structure for 3D Human Motions[C].13th International Multimedia Modeling Conference on Multimedia Modeling,2007:386-396.
[42]Pradhan GN, Balakrishnan Prabhakaran. Indexing 3-D Human Motion Repositories for Content-Based Retrieval[J]. IEEE Transaction on Information Technology In Biomedicine,2009,13(5):802-809.
[43]Chiu CY, Chao SP. Content-based retrieval for human motion data[J]. Journal of Visual Communication & Image Representation,2004,15446-466.
[44]Wu S, Xia S. Efficient motion data indexing and retrieval with local similarity measure of motion strings [J]. Visual Computer,2009,25(5):499-508.
[45]Wang X, Yu Z, Wong HS.3D Motion Sequence Retrieval Based On Data Distribution[C]. IEEE International Conference on Multimedia and Expo, Hannover,2008:1229-1232.
[46]Wang X, Yu Z, Wong HS. Searching of Motion Database Based On Hierarchical SOM[C]. IEEE International Conference on Multimedia and Expo, Hannover,2008:1233-1236.
[47]向坚,朱红丽.基于三维特征提取的人体运动数据分析和检索[J].计算机应用,2008,28(5)：1344-1346.
[48]Xiang J, Zhu HL. A Efficient Method for Motion Retrieval Based on Mocap Database[C]. IEEE International Conference on Control and Automation,2007:595-599.
[49]向坚,郭同强,吴飞,et al.基于双向参考索引的大规模人体运动数据库的检索[J].计算机研究与发展,2008,45(12)：2145-2153.
[50]张顺,梁秀波,李启雷,et al.示范表演驱动的运动数据检索方法及可用性评估[J].计算机辅助设计与图形学学报,2009,21(4)：554-560.
[51]Muller M, Roder T, Clausen M. Efficient content-based retrieval of motion capture data[C]. ACM Transactions on Graphics 2005:677-685.
[52]Muller M, Roder T. Motion Templates for Automatic Classification and Retrieval of Motion Capture Data[C]. ACM SIGGRAPH Symposium on Computer Animation 2006: 137-146.
[53]Demuth B, Roder T, Muller M. An Information Retrieval System for Motion Capture Data[C]. Proceedings of the 28th European Conference on Information Retrieval, 2006:373-384.
[54]Gao Y, Ma L, Chen Y. Content-Based Human Motion Retrieval with Automatic Transition[C].24th Computer Graphics International Conference, Hangzhou, China, 2006:360-371.
[55]Feng TC, Prabath Gunawardane. Motion Capture Data Retrieval Using an Artist's Doll[C].19th International Conference on Pattern Recognition,2008:1-4.
[56]潘红,肖俊,吴匕,et al.基于关键帧的三维人体运动检索[J].计算机辅助设计与图形学学报,2009,21(2)：214-222.
[57]Yueguo C, Shouxu J. Querying Complex Spatio-Temporal Sequences in Human Motion Databases [C]. IEEE 24th International Conference on Data Engineering 2008:90-99.
[58]Deng Z, Gu Q, Li Q. Perceptually Consistent Example-based Human Motion Retrieval [C]. Proceedings of the 2009 symposium on Interactive 3D Graphics and Games, Boston. Massachusetts,2009:191-198.
[59]Forbes K, Fiume E. An Efficient Search Algorithm for Motion Data Using Weighted PCA[C]. Eurographics/ACM SIGGRAPH Symposium on Computer Animation, Los Angeles, 2005:67-76.
[60]冯林,孙焘,于孝航,et al.基于运动能量模型的人体运动检索算法[J].计算机工程,2009,35(15)：26-28.
[61]冯林,沈骁,孙焘,et al.基于运动能量模型的人体运动捕捉数据库的检索[J].计算机辅助设计与图形学学报,2007,19(8)：1015-1021.
[62]Charly Awad, Nicolas Courty, Sylvie Gibet. A Database Architecture For Real-Time Motion Retrieval[C].7th International Workshop on Content-Based Multimedia Indexing, Chania,2009:225-230.
[63]Barbic J, Safonova A, Pan JY, et al. Segmenting motion capture data into distinct behaviors[C]. Proceedings of the 2004 conference on Graphics interface, Waterloo, Ontario, Canada,2004:185-194.
[64]Billon R, Nedelec A, Tisseau J. Gesture recognition in flow based on PCA analysis using multiagent system[C]. ACM International Conference, Yokohama. Japan,2008: 139-146.
[65]Xiang J, Zhu HL. A Novel Segmental Method for 3D Motion Data[C]. International Conference on Automation and Logistics. Qingdao. China2008.393-396.
[66]Jing SX, Wu Z, Huang ZY. Segmenting Single Actions from Continuous Captured Motion Sequences[C].2009 WRI World Congress on Computer Science and Information Engineering. Los Angeles2009.85-89.
[67]杨跃东,王莉莉,郝爱民,et al.基于几何特征的人体运动捕获数据分割方法[J].系统仿真学报,2007,19(10)：2229-2234.
[68]朱登明,王兆其.基于运动序列分割的运动捕获数据关键帧提取[J].计算机辅助设计与图形学学报,2008,20(6)：787-792.
[69]Peng SJ. Motion Segmentation Using Central Distance Features and Low-pass Filter[C].2010 International Conference:Computational Intelligence and Security (CIS),2010.223-226.
[70]Zhou F, Torre FD, Hodgins JK. Aligned Cluster Analysis for Temporal Segmentation of Human Motion[C].8th IEEE International Conference:Automatic Face & Gesture Recognition. Amsterdam2008.1-7.
[71]Koray Balci, Lale Akarun. Clustering Poses of Motion Capture Data Using Limb Centroids[C].23rd International Symposium:Computer and Information Sciences. 2008.1-6.
[72]Dana Kuli'c, Wataru Takano, Yoshihiko Nakamura. Combining Automated On-line Segmentation and Incremental Clustering for Whole Body Motions[C]. IEEE International Conference:Robotics and Automation 2008.2008.2591-2598.
[73]杨跃东,王莉莉,郝爱民.运动串：一种用于行为分割的运动捕获数据表示方法[J].计算机研究与发展,2008,45(3)：527-534.
[74]Yang AY, Sameer Iyengar, Philip Kuryloski, et al. Distributed Segmentation and Classification of Human Actions Using a Wearable Motion Sensor Network[C]. IEEE Computer Society Conference:Computer Vision and Pattern Recognition Workshops. 2008.1-8.
[75]Yan Wang, Hyewon Seo, Soohyun Jeon. Automatic Transition Detection of Segmented Motion Clips Using PCA-based GMM Method[C]. Cyberworlds:2008 International Conference.2008.567-572.
[76]Kanav Kahol, Priyamvada Tripathi, Sethuraman Panchanathan, et al. GESTURE SEGMENTATION IN COMPLEX MOTION SEQUENCES[C]. ICIP 2003 International Conference. 2003.105-108.
[77]Ajo Fod, Maja J Mataric, Odest Chadwicke Jenkins. Automated Derivation of Primitives for Movement Classification[J]. Autonomous Robots,2002,12(1): 39-54.
[78]Gibet S, Marteau PF. Analysis of human motion, based on the reduction of multidimensional captured data-application to hand gesture compression, segmentation and synthesis[J]. Articulated Motion and Deformable Objects,2008, 72-81.
[79]Gibet S, Marteau PF. Approximation of Curvature and Velocity for Gesture Segmentation and Synthesis[C]. Gesture-Based Human-Computer Interaction and Simulation. Springer,2009.13-23.
[80]Li C, Zhai P, Zheng SQ, et al. Segmentation and Recognition of Multi-Attribute Motion Sequences[C]. International Multimedia Conference, Proceedings of the 12th annual ACM international conference, New York, USA,2004:836-843.
[81]LI C, ZHENG SQ, PRABHAKARAN B. Segmentation and Recognition of Motion Streams by Similarity Search[J]. ACM Transactions on Multimedia Computing, Communications, and Applications,2007,3(3):16-es.
[82]肖俊,庄越挺,吴飞.三维人体运动特征可视化与交互式运动分割[J].软件学报,2008,19(8)：1995-2003.
[83]Schulz S, Woerner A. Automatic motion segmentation for human motion synthesis[C]. Articulated Motion and Deformable Objects. Springer,2010.182-191.
[84]Wang M, Wang W, Li Y, et al. Motion capture data segmentation using kernel dynamic texture[C]. Audio Language and Image Processing (ICALIP),2010:592-596.
[85]Bodenheimer B, Rose C, Rosenthal S, et al. The process of motion capture:Dealing with the data[C]. Eurographics CAS'97,1997:3-18.
[86]Choi K, Park S, Ko H. Processing Motion Capture Data to Achieve Positional Accuracy[J]. Graphical Models and Image Processing,1999,61(5):260-273.
[87]Unuma M, Anjyo K, Takeuchi R. Fourier principles for emotion-based human figure animation[C]. The SIGGRAPH'95, Los Angeles,1995:91-96.
[88]Bruderlin A, Williams L. Motion signal processing[C]. The 22nd Annual Conference on Computer Graphics, Los Angeles,1995:97-104.
[89]Witkin A, Popovic Z. Motion warping[C]. The 22nd Annual Conference on Computer Graphics., LosAngeles,1995:105-108.
[90]Choi K, Ko H. On-l ine motion retargeting[J]. Journal of Visualization and Computer Animation,2000,11(5):223-243.
[91]Gleicher M. Motion editing with spacetime Constraints[C]. The 1997 Symposium on Interactive 3D Graphics., New York,1997:139-148.
[92]Witkin A, KassM. Spacetime constraints[J]. Computer Graphics,1988,22(4): 159-168.
[93]Gleicher M. Retargetting motion to new characters[C]. ACM SIGGRAPH, Orlando, 1998:33-42.
[94]Rose C, Guenter B, Bodenheimer B. Efficient generation of motion transitions using spacetime constraints[C]. ACM The 23 rd annual conference, New Orleans,1996: 147-154.
[95]Popovic Z, Witkin A. Physically based motion transformation[C]. The 26th Annual Conference on Computer Graphics, LosAngeles,1999:11-20.
[96]Pollard NS, Hodgins JK, Riley MJ, et al. Adapting human motion for the control of a humanoid robot [C]. IEEE International Conference on Robotics and Automation,, 2002:1390-1397.
[97]Nakaoka S, Nakazawa A, Yokoi K, et al. Generating whole body motions for a biped humanoid robot from captured human dances[C]. IEEE International Conference on Robotics and Automation,2003:3905-3910.
[98]Kim S, Kim CH, Park JH. Human-like arm motion generation for humanoid robots using motion capture database[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems,2006:3486-3491.
[99]Ott C, Lee D, Nakamura Y. Motion capture based human motion recognition and imitation by direct marker control [C].8th IEEE-RAS International Conference on Humanoid Robots,2008:399-405.
[100]赵晓军,黄强,彭朝琴,et al.基于人体运动的仿人型机器人动作的运动学匹配[J].机器人,2005,27(4)：358-361.
[101]Moeslund TB, Granum E. A survey of computer vision-based human motion capture[J]. Computer Vision and Image Understanding,2001,81(3):231-268.
[102]东方新锐三维运动捕捉／分析系统http://www.dorealsoft.com/product/
[103]CMU Graphics Lab Motion Capture Database http://mocap.cs.cmu.edu/
[104]Motion Capture Database HDM05. http://www.mpi-inf.mpg.de/resources/HDM05/
[105]Biovision BVH. http://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/BVH.html.
[106]Meredith M, Maddock S. Motion capture file formats explained[J]. Department of Computer Science, University of Sheffielf,2001.
[107]Acclaim ASF/AMC. http://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html.
[108]Arun KS, HUANG TS, BLOSTEIN SD. Least-Squares Fitting of Two 3-D Point Sets[J]. IEEE Transactions On Pattern Analysis and Machine Intelligence,1987,9(5): 698-700.
[109]L. Boxer. Faster point set pattern matching in 3-D[J]. Pattern Recognition Letters, 1998,191235-1240.
[110]袁建平,朱战霞.空间操作与非开普勒运动[J].宇航学报,2009,30(1)：42-46.
[111]杨磊,何克忠,郭木河,et al.虚拟现实技术在机器人技术中的应用与展望[J].机器人,1998,20(1)：76-80.
[112]庄骏,年平,孙增沂.大时延环境下的分布式遥操作系统[J].清华大学学报：自然科学版,2000,40(1)：80-83.
[113]Qi J, Miao Z, Wang Z, et al. Several methods of smoothing motion capture data[C]. Third International Conference on Digital Image Processing (ICDIP 2011),2011: 80091E1-5.
[114]John GP, Dimitris GM. Digital Signal Processing:Principles, Algorithms, and Applications[M]. Prentice Hall.
[2]蔡琪,郭静博.动漫产业发展及其计算机技术探讨[J].信息与电脑,2011,(09)：15-17.
[3]2007-2008年世界动漫产业发展研究年度总报告.http://market.ccidnet.com/
[4]“十二五”规划助力动漫产业春天正在到来.http://www.ce.cn/
[5]Sturman DJ. A brief history of motion capture for computer character animation[J]. SIGGRAPH94, Course9,1994.
[6]Calvert TW, Chapman J, Patla A. Aspects of the kinematic simulation of human movement[J]. IEEE Computer Graphics and Applications,1982,2(9):41-50.
[7]Ginsberg CM, Maxwell D. Graphical marionette[C]. In:Proc. ACM SIGGRAPH/SIGART Workshop on Motion,1983:172-179.
[8]Robertson B. Mike, the talking head[J]. Computer Graphics World,1989,11(7): 15-17.
[9]Walters G. The story of Waldo C. Graphic[C]. SIGGRAPH' 89. ACM Press,1989.65-79.
[10]Walters G. Performance animation at PDI [C]. SIGGRAPH'93. ACM Press,1993.40-53.
[11]Kleiser J. Character motion systems[C]. SIGGRAPH'93. ACM Press,1993.33-36.
[12]Tardif H. Character animation in real time[C]. SIGGRAPH Panel Proceedings, I: Reports from the Field,1991.
[13]Robertson B. Moving pictures[J]. Computer Graphics World,1992,15(10):38-44.
[14]Sang Il Park, Jessica K. Hodgins. Capturing and Animating Skin Deformation in Human Mot ion[J]. ACM Transaction on Graphics,2006,25(3):881-889.
[15]HSIEH CC., KUO PL. An impulsive noise reduction agent for rigid bodymotion data usingB-spline wavelets[J]. Expert Systems with Applications,2008,34(3): 1733-1741.
[16]Alonso FJ, Del Castillo JM, Pintado P. Motion data processing and wobbling mass modelling in the inverse dynamics of skeletal models[J]. Mechanism and Machine Theory,2007,42(9):1153-1169.
[17]高申玉,刘金刚.人体运动实时捕捉设备传输数据的滤波与处理[J].计算机工程与设计,2006,27(15)：2715-2717.
[18]Aissaoui R, Husse S, Mecheri H, et al. Automatic filtering techniques for three-dimensional kinematics data using 3D motion capture system[C]. Proceeding of IEEE International Symposium on Industrial Electronics,2006:614-619.
[19]夏时洪,魏毅,王兆其.一种对人体运动捕获数据中的噪声和误差进行修正的方法[P].中国,专利号：200610113071.8,2006.
[20]Adam G.Kirk, James F.O Brien, David A. Forsyth. Skeletal Parameter Estimation from Optical Motion Capture Data[C]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05),2005:782-788.
[21]Maurice Ringer, Joan Lasenby. Multiple Hypothesis Tracking for Automatic Optical Motion Capture[C]. Computer Vision — ECCV 2002. Springer,2002.524-536.
[22]Herda L, Fua P, Plankers R, et al. Skeleton-based motion capture for robust reconstruction of human motion[C]. Proceedings of Computer Animation 2000,2000: 77-83.
[23]汪亚明,楼正国,卞昕,et a1.一种非刚体运动图像序列的特征点对应方法[J].中国图象图形学报,2000,5(3)：232-236.
[24]Antonio CS, Luis EL, Jose RFB, et al. A motion capture system using passive markers[C]. Proceedings of the 2004 ACM SIGGRAPH international conference,2004: 440-447.
[25]黄波十,陈福民,张金剑.基于序列帧的光学运动捕捉系统及实现[J].计算机工程与应用,2004,40(17)：36-39.
[26]黄波十,陈福民，张金剑.一种改进算法的光学运动捕捉系统[J].同济大学学报(自然科学版),2005,33(10)：1372-1376.
[27]Kurihara K, Hoshino S, Yamane K, et al. Optical mot ion capture system with pan-tilt camera tracking and real time data processing[C].2002 IEEE, International Conference on Robotics & Automation,2002:1241-1248.
[28]Horst Holstein, Baihua Li. Low Density Feature Point Matching for Articulated Pose Identification[C]. British Machine Vision Conference.2002.678-687.
[29]Li BH, Meng Q, Horst Holstein. Reconstruction of segmentally articulated structure in freeform movement with low density feature points[J]. Image and Vision Computing 2004,22749-759.
[30]Li BH, Meng Q, Horst Holstein. Articulated motion reconstruction from feature points[J]. Pattern Recognition Letters,2008,41418-431.
[31]肖伯祥,魏小鹏,张强,et a1.光学运动捕捉散乱数据处理的一种方法[J].系统仿真学报,2008,20(2)：382-385.
[32]Herda L, Fua P, Plankers R, et al. Skeleton-based motion capture for robust reconstruction of human motion[C]. Proceeding of Computer Animation, Washington DC,2000:77-83.
[33]Hornung A, Sar-Dessai S, Kobbelt L. Self-calibrating optical motion tracking for articulated bodies[C]. Proceeding of IEEE Virtual Reality Conference,2005: 75-82.
[34]Koh S, Kim C. Geometrical fitting of missing data for shape from motion under noise distribution[J]. Advances in Natural Computation,2006,774-783.
[35]Liu G, Zhang J, Wang W, et al. Human motion estimation from a reduced marker set [C]. Proc ofACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, California, 2006:35-42.
[36]Liu G, McMillan L. Estimation of missing markers in human motion capture [J]. The Visual Computer,2006,22(9):721-728.
[37]Zhuang YT, Pan YH, Xiao J.Intelligent Techniques for Processing and Management of Motion Data. A Modern Approach to Intelligent Animation, Springer Berlin Heidelberg,2008.
[38]刘丰,庄越挺,吴飞,et a1.基于例子的三维运动检索[J].计算机辅助设计与图形学学报,2003,15(10)：1275-1280.
[39]Liu F, Zhuang YT, Wu F.3D motion retrieval with motion index tree[J]. Computer Vision and Image Understanding,2003,92(2):265-284.
[40]Lin Y. Efficient Motion Search in Large Motion Capture Databases[C]. Second International Symposium on Visual Computing,2006:151-160.
[41]Pradhan GN, Li CJ, Balakrishnan Prabhakaran. Hierarchical Indexing Structure for 3D Human Motions[C].13th International Multimedia Modeling Conference on Multimedia Modeling,2007:386-396.
[42]Pradhan GN, Balakrishnan Prabhakaran. Indexing 3-D Human Motion Repositories for Content-Based Retrieval[J]. IEEE Transaction on Information Technology In Biomedicine,2009,13(5):802-809.
[43]Chiu CY, Chao SP. Content-based retrieval for human motion data[J]. Journal of Visual Communication & Image Representation,2004,15446-466.
[44]Wu S, Xia S. Efficient motion data indexing and retrieval with local similarity measure of motion strings [J]. Visual Computer,2009,25(5):499-508.
[45]Wang X, Yu Z, Wong HS.3D Motion Sequence Retrieval Based On Data Distribution[C]. IEEE International Conference on Multimedia and Expo, Hannover,2008:1229-1232.
[46]Wang X, Yu Z, Wong HS. Searching of Motion Database Based On Hierarchical SOM[C]. IEEE International Conference on Multimedia and Expo, Hannover,2008:1233-1236.
[47]向坚,朱红丽.基于三维特征提取的人体运动数据分析和检索[J].计算机应用,2008,28(5)：1344-1346.
[48]Xiang J, Zhu HL. A Efficient Method for Motion Retrieval Based on Mocap Database[C]. IEEE International Conference on Control and Automation,2007:595-599.
[49]向坚,郭同强,吴飞,et al.基于双向参考索引的大规模人体运动数据库的检索[J].计算机研究与发展,2008,45(12)：2145-2153.
[50]张顺,梁秀波,李启雷,et al.示范表演驱动的运动数据检索方法及可用性评估[J].计算机辅助设计与图形学学报,2009,21(4)：554-560.
[51]Muller M, Roder T, Clausen M. Efficient content-based retrieval of motion capture data[C]. ACM Transactions on Graphics 2005:677-685.
[52]Muller M, Roder T. Motion Templates for Automatic Classification and Retrieval of Motion Capture Data[C]. ACM SIGGRAPH Symposium on Computer Animation 2006: 137-146.
[53]Demuth B, Roder T, Muller M. An Information Retrieval System for Motion Capture Data[C]. Proceedings of the 28th European Conference on Information Retrieval, 2006:373-384.
[54]Gao Y, Ma L, Chen Y. Content-Based Human Motion Retrieval with Automatic Transition[C].24th Computer Graphics International Conference, Hangzhou, China, 2006:360-371.
[55]Feng TC, Prabath Gunawardane. Motion Capture Data Retrieval Using an Artist's Doll[C].19th International Conference on Pattern Recognition,2008:1-4.
[56]潘红,肖俊,吴匕,et al.基于关键帧的三维人体运动检索[J].计算机辅助设计与图形学学报,2009,21(2)：214-222.
[57]Yueguo C, Shouxu J. Querying Complex Spatio-Temporal Sequences in Human Motion Databases [C]. IEEE 24th International Conference on Data Engineering 2008:90-99.
[58]Deng Z, Gu Q, Li Q. Perceptually Consistent Example-based Human Motion Retrieval [C]. Proceedings of the 2009 symposium on Interactive 3D Graphics and Games, Boston. Massachusetts,2009:191-198.
[59]Forbes K, Fiume E. An Efficient Search Algorithm for Motion Data Using Weighted PCA[C]. Eurographics/ACM SIGGRAPH Symposium on Computer Animation, Los Angeles, 2005:67-76.
[60]冯林,孙焘,于孝航,et al.基于运动能量模型的人体运动检索算法[J].计算机工程,2009,35(15)：26-28.
[61]冯林,沈骁,孙焘,et al.基于运动能量模型的人体运动捕捉数据库的检索[J].计算机辅助设计与图形学学报,2007,19(8)：1015-1021.
[62]Charly Awad, Nicolas Courty, Sylvie Gibet. A Database Architecture For Real-Time Motion Retrieval[C].7th International Workshop on Content-Based Multimedia Indexing, Chania,2009:225-230.
[63]Barbic J, Safonova A, Pan JY, et al. Segmenting motion capture data into distinct behaviors[C]. Proceedings of the 2004 conference on Graphics interface, Waterloo, Ontario, Canada,2004:185-194.
[64]Billon R, Nedelec A, Tisseau J. Gesture recognition in flow based on PCA analysis using multiagent system[C]. ACM International Conference, Yokohama. Japan,2008: 139-146.
[65]Xiang J, Zhu HL. A Novel Segmental Method for 3D Motion Data[C]. International Conference on Automation and Logistics. Qingdao. China2008.393-396.
[66]Jing SX, Wu Z, Huang ZY. Segmenting Single Actions from Continuous Captured Motion Sequences[C].2009 WRI World Congress on Computer Science and Information Engineering. Los Angeles2009.85-89.
[67]杨跃东,王莉莉,郝爱民,et al.基于几何特征的人体运动捕获数据分割方法[J].系统仿真学报,2007,19(10)：2229-2234.
[68]朱登明,王兆其.基于运动序列分割的运动捕获数据关键帧提取[J].计算机辅助设计与图形学学报,2008,20(6)：787-792.
[69]Peng SJ. Motion Segmentation Using Central Distance Features and Low-pass Filter[C].2010 International Conference:Computational Intelligence and Security (CIS),2010.223-226.
[70]Zhou F, Torre FD, Hodgins JK. Aligned Cluster Analysis for Temporal Segmentation of Human Motion[C].8th IEEE International Conference:Automatic Face & Gesture Recognition. Amsterdam2008.1-7.
[71]Koray Balci, Lale Akarun. Clustering Poses of Motion Capture Data Using Limb Centroids[C].23rd International Symposium:Computer and Information Sciences. 2008.1-6.
[72]Dana Kuli'c, Wataru Takano, Yoshihiko Nakamura. Combining Automated On-line Segmentation and Incremental Clustering for Whole Body Motions[C]. IEEE International Conference:Robotics and Automation 2008.2008.2591-2598.
[73]杨跃东,王莉莉,郝爱民.运动串：一种用于行为分割的运动捕获数据表示方法[J].计算机研究与发展,2008,45(3)：527-534.
[74]Yang AY, Sameer Iyengar, Philip Kuryloski, et al. Distributed Segmentation and Classification of Human Actions Using a Wearable Motion Sensor Network[C]. IEEE Computer Society Conference:Computer Vision and Pattern Recognition Workshops. 2008.1-8.
[75]Yan Wang, Hyewon Seo, Soohyun Jeon. Automatic Transition Detection of Segmented Motion Clips Using PCA-based GMM Method[C]. Cyberworlds:2008 International Conference.2008.567-572.
[76]Kanav Kahol, Priyamvada Tripathi, Sethuraman Panchanathan, et al. GESTURE SEGMENTATION IN COMPLEX MOTION SEQUENCES[C]. ICIP 2003 International Conference. 2003.105-108.
[77]Ajo Fod, Maja J Mataric, Odest Chadwicke Jenkins. Automated Derivation of Primitives for Movement Classification[J]. Autonomous Robots,2002,12(1): 39-54.
[78]Gibet S, Marteau PF. Analysis of human motion, based on the reduction of multidimensional captured data-application to hand gesture compression, segmentation and synthesis[J]. Articulated Motion and Deformable Objects,2008, 72-81.
[79]Gibet S, Marteau PF. Approximation of Curvature and Velocity for Gesture Segmentation and Synthesis[C]. Gesture-Based Human-Computer Interaction and Simulation. Springer,2009.13-23.
[80]Li C, Zhai P, Zheng SQ, et al. Segmentation and Recognition of Multi-Attribute Motion Sequences[C]. International Multimedia Conference, Proceedings of the 12th annual ACM international conference, New York, USA,2004:836-843.
[81]LI C, ZHENG SQ, PRABHAKARAN B. Segmentation and Recognition of Motion Streams by Similarity Search[J]. ACM Transactions on Multimedia Computing, Communications, and Applications,2007,3(3):16-es.
[82]肖俊,庄越挺,吴飞.三维人体运动特征可视化与交互式运动分割[J].软件学报,2008,19(8)：1995-2003.
[83]Schulz S, Woerner A. Automatic motion segmentation for human motion synthesis[C]. Articulated Motion and Deformable Objects. Springer,2010.182-191.
[84]Wang M, Wang W, Li Y, et al. Motion capture data segmentation using kernel dynamic texture[C]. Audio Language and Image Processing (ICALIP),2010:592-596.
[85]Bodenheimer B, Rose C, Rosenthal S, et al. The process of motion capture:Dealing with the data[C]. Eurographics CAS'97,1997:3-18.
[86]Choi K, Park S, Ko H. Processing Motion Capture Data to Achieve Positional Accuracy[J]. Graphical Models and Image Processing,1999,61(5):260-273.
[87]Unuma M, Anjyo K, Takeuchi R. Fourier principles for emotion-based human figure animation[C]. The SIGGRAPH'95, Los Angeles,1995:91-96.
[88]Bruderlin A, Williams L. Motion signal processing[C]. The 22nd Annual Conference on Computer Graphics, Los Angeles,1995:97-104.
[89]Witkin A, Popovic Z. Motion warping[C]. The 22nd Annual Conference on Computer Graphics., LosAngeles,1995:105-108.
[90]Choi K, Ko H. On-l ine motion retargeting[J]. Journal of Visualization and Computer Animation,2000,11(5):223-243.
[91]Gleicher M. Motion editing with spacetime Constraints[C]. The 1997 Symposium on Interactive 3D Graphics., New York,1997:139-148.
[92]Witkin A, KassM. Spacetime constraints[J]. Computer Graphics,1988,22(4): 159-168.
[93]Gleicher M. Retargetting motion to new characters[C]. ACM SIGGRAPH, Orlando, 1998:33-42.
[94]Rose C, Guenter B, Bodenheimer B. Efficient generation of motion transitions using spacetime constraints[C]. ACM The 23 rd annual conference, New Orleans,1996: 147-154.
[95]Popovic Z, Witkin A. Physically based motion transformation[C]. The 26th Annual Conference on Computer Graphics, LosAngeles,1999:11-20.
[96]Pollard NS, Hodgins JK, Riley MJ, et al. Adapting human motion for the control of a humanoid robot [C]. IEEE International Conference on Robotics and Automation,, 2002:1390-1397.
[97]Nakaoka S, Nakazawa A, Yokoi K, et al. Generating whole body motions for a biped humanoid robot from captured human dances[C]. IEEE International Conference on Robotics and Automation,2003:3905-3910.
[98]Kim S, Kim CH, Park JH. Human-like arm motion generation for humanoid robots using motion capture database[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems,2006:3486-3491.
[99]Ott C, Lee D, Nakamura Y. Motion capture based human motion recognition and imitation by direct marker control [C].8th IEEE-RAS International Conference on Humanoid Robots,2008:399-405.
[100]赵晓军,黄强,彭朝琴,et al.基于人体运动的仿人型机器人动作的运动学匹配[J].机器人,2005,27(4)：358-361.
[101]Moeslund TB, Granum E. A survey of computer vision-based human motion capture[J]. Computer Vision and Image Understanding,2001,81(3):231-268.
[102]东方新锐三维运动捕捉／分析系统http://www.dorealsoft.com/product/
[103]CMU Graphics Lab Motion Capture Database http://mocap.cs.cmu.edu/
[104]Motion Capture Database HDM05. http://www.mpi-inf.mpg.de/resources/HDM05/
[105]Biovision BVH. http://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/BVH.html.
[106]Meredith M, Maddock S. Motion capture file formats explained[J]. Department of Computer Science, University of Sheffielf,2001.
[107]Acclaim ASF/AMC. http://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html.
[108]Arun KS, HUANG TS, BLOSTEIN SD. Least-Squares Fitting of Two 3-D Point Sets[J]. IEEE Transactions On Pattern Analysis and Machine Intelligence,1987,9(5): 698-700.
[109]L. Boxer. Faster point set pattern matching in 3-D[J]. Pattern Recognition Letters, 1998,191235-1240.
[110]袁建平,朱战霞.空间操作与非开普勒运动[J].宇航学报,2009,30(1)：42-46.
[111]杨磊,何克忠,郭木河,et al.虚拟现实技术在机器人技术中的应用与展望[J].机器人,1998,20(1)：76-80.
[112]庄骏,年平,孙增沂.大时延环境下的分布式遥操作系统[J].清华大学学报：自然科学版,2000,40(1)：80-83.
[113]Qi J, Miao Z, Wang Z, et al. Several methods of smoothing motion capture data[C]. Third International Conference on Digital Image Processing (ICDIP 2011),2011: 80091E1-5.
[114]John GP, Dimitris GM. Digital Signal Processing:Principles, Algorithms, and Applications[M]. Prentice Hall.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |