Nonrigid motion analysis based on dynamic refinement of finite element models

In this paper we propose new algorithms for accurate nonrigid motion tracking. Given only a set of sparse correspondences and incomplete or missing information about geometry or material properties, we recover dense motion vectors using nonlinear finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behavior. Large differences indicate that an object's properties are not captured properly by the model. Feedback from the images during the motion allows the refinement of the model by minimizing the error between the expected and true position of the object's points. Unknown parameters are recovered using an iterative descent search for the best model that approximates nonrigid motion of the given object. Thus, during tracking the model is refined which, in turn, improves tracking quality. The method was applied successfully to man-made elastic materials and human skin to recover unknown elasticity, to complex 3-D objects to find details of their geometry, and to a hand motion analysis application.

[1]  Thacker Jg,et al.  Three-dimensional computer model of the human buttocks, in vivo , 1994 .

[2]  Gábor Székely,et al.  Deformable Velcro surfaces , 1995, Proceedings of IEEE International Conference on Computer Vision.

[3]  D S Childress,et al.  Modelling the mechanics of narrowly contained soft tissues: the effects of specification of Poisson's ratio. , 1993, Journal of rehabilitation research and development.

[4]  W. Larrabee.  A finite element model of skin deformation. I. Biomechanics of skin and soft tissue: A review , 1986, The Laryngoscope.

[5]  Dmitry B. Goldgof,et al.  Model-based force-driven nonrigid motion recovery from sequences of range images without point correspondences , 1999, Image Vis. Comput..

[6]  J B Lucas,et al.  The National Institute for Occupational Safety and Health , 1977, Contact dermatitis.

[7]  Alistair A. Young,et al.  Non-rigid heart wall motion using MR tagging , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Dmitry B. Goldgof,et al.  Efficient Nonlinear Finite Element Modeling of Nonrigid Objects via Optimization of Mesh Models , 1998, Comput. Vis. Image Underst..

[9]  Ruediger Dillmann,et al.  Human Motion Analysis: A Review , 1997 .

[10]  Nicholas Ayache,et al.  Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Takeo Kanade,et al.  Visual Tracking of High DOF Articulated Structures: an Application to Human Hand Tracking , 1994, ECCV.

[12]  George Brant Bridgman.  The Human Machine , 1972 .

[13]  V. C. Roberts,et al.  Development of a non-linear finite element modelling of the below-knee prosthetic socket interface. , 1995, Medical engineering & physics.

[14]  Dmitry B. Goldgof,et al.  A vision-based technique for objective assessment of burn scars , 1998, IEEE Transactions on Medical Imaging.

[15]  Alex Pentland,et al.  A three-dimensional model of human lip motions trained from video , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[16]  Dimitri Metaxas,et al.  Efficient shape representation using deformable models with locally adaptive finite elements , 1993, Optics & Photonics.

[17]  A. Khotanzad,et al.  A physics-based coordinate transformation for 3-D image matching , 1997, IEEE Transactions on Medical Imaging.

[18]  D S Childress,et al.  A review of prosthetic interface stress investigations. , 1996, Journal of rehabilitation research and development.

[19]  Dimitris N. Metaxas,et al.  Constrained deformable superquadrics and nonrigid motion tracking , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[21]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[22]  Takeo Kanade,et al.  Model-based tracking of self-occluding articulated objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[23]  Dimitris N. Metaxas,et al.  Adaptive shape evolution using blending , 1995, Proceedings of IEEE International Conference on Computer Vision.

[24]  Thomas S. Huang,et al.  Vision based hand modeling and tracking for virtual teleconferencing and telecollaboration , 1995, Proceedings of IEEE International Conference on Computer Vision.

[25]  George Brant Bridgman,et al.  Bridgman's Complete Guide to Drawing from Life , 1971 .

[26]  I. Babuska,et al.  Finite Element Analysis , 2021 .

[27]  A. Pentland,et al.  Physically-based combinations of views: representing rigid and nonrigid motion , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[28]  Siavouche Nemat-Nasser,et al.  Theoretical foundation for large-scale computations for nonlinear material behavior , 1984 .

[29]  J G Thacker,et al.  Three-dimensional computer model of the human buttocks, in vivo. , 1994, Journal of rehabilitation research and development.

[30]  Y. Kita,et al.  Elastic-Model Driven Analysis of Several Views of a Deformable Cylindrical Object , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  John R. Brauer.  What every engineer should know about finite element analysis , 1995 .

[33]  H. Delingette.  Adaptive and deformable models based on simplex meshes , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[34]  S. Sarkar,et al.  Human skin and hand motion analysis from range image sequences using nonlinear FEM , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[35]  D. Griffin,et al.  Finite-Element Analysis , 1975 .