Elastic-Model Driven Analysis of Several Views of a Deformable Cylindrical Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient nonlinear finite element modeling of nonrigid objects via optimization of mesh models
Computer Vision and Image Understanding - Special issue on CAD-based computer vision
Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive shape evolution using blending
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Vision based hand modeling and tracking for virtual teleconferencing and telecollaboration
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Three-Dimensional Model of Human Lip Motions Trained from Video
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Human Skin and Hand Motion Analysis from Range Image Sequences Using Nonlinear FEM
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Human Motion Analysis: A Review
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
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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.