期刊文献+

基于体素的人手结构自动估测 被引量:2

Automatic calibration of hand structures based on voxel data
原文传递
导出
摘要 目的人手结构估测是建立个性化3维人手模型以及进行人手姿态估测的重要步骤之一。由于人手骨架为表皮所覆盖,普通摄像机无法直接获得其结构,因此常常需要人员参与估测过程,无法真正实现自动化,这必然会增加估测的时间和估测的复杂度。方法本文从人手纹理与人手关节位置相关性出发,利用一套图像分割和图像增强技术提取出大致的人手连接结构;再利用多视点3维建模、3维拟合技术和粒子群优化算法,优化之前得到的人手连接结构。结果经实验证明该方法能成功地提取出人手结构,估测的每个阶段均无需人员参与。结论与之前方法相比,该方法真正实现了估测过程自动化,减少了估测时间和估测误差。 Objective Calibration of band structures is one of important steps to create an individualized three-dimensional hand model and estimate hand-pose. Because the hand structure is covered by the hand surface, the common cameras can- not directly get the hand structure, and people were required to participate in the calibration of hand structure, which can- not be completely processed by the computer. That makes the calibration complex and more time is needed. Method In or- der to acquire an automatic calibration of a hand structure, a set of image segmentation and enhancement techniques were used to find the link structure of the hand, based on the relationship of hand prints and joints in the individual' s hand. The techniques of 3D modeling using multiple cameras, 3D fitting, and the Particle Swarm Optimization algorithm were used to optimize the link structure of hand. Result Proved by the experiments, the calibration method proposed in this pa- per can successfully extract the structure of the hand, and each stage of the calibration process doesn' t need people partic- ipating in. Conclusion Comparing with the original method, the calibration method proposed in this paper is completed by the computer. As a result the calibration time is saved and errors are reduced.
出处 《中国图象图形学报》 CSCD 北大核心 2014年第1期54-61,共8页 Journal of Image and Graphics
基金 国家自然科学基金面上项目(61173124) 浙江省教育厅年度科研项目(Y201120954)
关键词 个性化 连接结构 体素 人手模型 手姿态估测 individuation link structure of hand voxel data hand model hand-pose estimation
  • 相关文献

参考文献16

  • 1Pan Z G, l,i Y, Zhang M M, et al. A real-lime multi-cue hand tracking algorithm based on Omlult'r vision[ C ]// Proceeding ofIEEE Virtual Reality 2010. Waltham-Boston : IEEE Press, 2010 : 219-222.
  • 2Stiefelhagen R, Fugen C, Gieselmann R, et al. Natural human- robot interaction using speech, head pose and gestures [ C ]//Pro- ceedings of IEEE/RSJ Conference on Intelligent Robots and Sys- tems. Sendai : IEEE Press, 2004:2422-2427.
  • 3Feng Z Q, Zhang M M, Pan Z G, et al. 3D-freehand-pose ini- tialization based on operator' s cognitive behavioral models [ J ]. The Visual Computer, 2010,26(6-8) : 607-617.
  • 4Zhao W P, Chai J X, Xu Y Q. Combining marker-based mocap and RGB-D camera for acquiring high-fidelity hand motion data [ C]//The ACM SIGGRAPH/Eurographics Symposium on Com- puter Animation .Symposium on Computer Animation. Lausanne : The Eurographics Association, 2012:33-42.
  • 5Romero J, Kjellstrom h, Kragic d. Hands in action:real-time 3D reconstruction hands in interaction with objects [ C] //Proceedings of IEEE International Conference on Robotics and Automation. Anchorage : IEEE Press, 2010:458-463.
  • 6Etsuko U, Yoshio M, Masakazu I, et al. A hand-pose estimation for vision-vased human interfaces[J]. IEEE Transactions on In- duetrial Electronics, 2003, 50(4) :676-684.
  • 7Kofman J, Wu X H, Luu T J, et al. Teleoperation of a robot ma- nipulator using a vision-based human-robot interface [ J 1- IEEE Transactions on Industrial Electronics, 2005, 52 ( 5 ) : 1206- 1219.
  • 8Wu Y, Lin J Y, Huang T S. Capturing natural hand articulation [ C l//Proceedings of International Conference on Computer Vision. Vancouver:IEEE Computer Society, 2001:426-432.
  • 9Stenger B, Mendonca P R S, Cipolla R. Model based 3d track- ing of an articulated hand [ C ]//Proceedings of Conference inComputer Vision and Pattern Recognition. Kauai : IEEE Computer Society, 2001:310-315.
  • 10Kurihara T, Miyata M. Modeling deformable human hands from medical images[ C]//The ACM SIGGRAPH/Eurographics Sym- posium on Computer Animation. Grenoble: The Eurographics Association, 2004:355-363.

二级参考文献24

  • 1Barbara Gengler. The future of palmprint [ J ]. Network Security,1999, 12(3): 5-6.
  • 2Arun Ross, Anil Jain. Information fusion in biometrics [ J]. Pattern Reeognition Letters, 2003, 24( 13 ) : 2115 - 2125.
  • 3Wang Y K. Dermatoglyphics and Clinic [ M ]. Beijing: World Publishing Co. Ltd, 1999:3 -4.
  • 4Zhang Da-peng, Shu Wei. Two novel characteristics in plamprint verification[J]. Pattern Recognition, 1999, 32(4): 691 -702.
  • 5Han Chin-chuan, Cheng Hsu-liang, Lin Chih-lung. Kuo-Chin Personal authentication using palm-print features [ J ]. Pattern Recognition, 2003, 3fi(2) : 371 -381.
  • 6王涛,李文新,赵蔚楠等.一种基于小波变换的掌纹识别新方法[A].见:第四届中国生物识别学术会议[C],北京,2003:105—109.
  • 7Wn X, Wang K, David Zhang. A novel approach of palm-line extraction [ A ]. In : Proceedings of the Third International Conference on Image and Graphics[ C ] , Hongkong, 2004 : 230 - 233.
  • 8Wu Xiang-qian, Zhang David, Wang Kuan-quan, et al. Palmprint classification using principal lines [ J ]. Pattern Recognition, 2004,37(10) : 1987 - 1998.
  • 9Lim Jae S. Two-Dimensional Sgnal and Image Processing [ M ].Englewood Cliffs, NJ: Prentice Hall, 1990:536 - 540.
  • 10Gonzalez Woods. Digital Image Processing[ M ]. Beijing: Publishing House of Electronics Industry, 2002:489 - 492.

共引文献13

同被引文献10

引证文献2

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部