A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Feature extraction from faces using deformable templates
International Journal of Computer Vision
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
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
Robust Parameter Estimation in Computer Vision
SIAM Review
Programming the Boundary Element Method
Programming the Boundary Element Method
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Vision-Based Force Measurement
IEEE Transactions on Pattern Analysis and Machine Intelligence
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
International Journal of Computer Vision
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The manipulation of deformable objects is an important problem in robotics and arises in many applications including biomanipulation, microassembly, and robotic surgery. For some applications, the robotic manipulator itself may be deformable. Vision-based deformable object tracking can provide feedback for these applications. Computer vision is a logical sensing choice for tracking deformable objects because the large amount of data that is collected by a vision system allows many points within the deformable object to be tracked simultaneously. This article introduces a template based deformable object tracking algorithm, based on the boundary element method, that is able to track a wide range of deformable objects. The robustness of this algorithm to occlusions and to spurious edges in the source image is also demonstrated. A robust error measure is used to handle the problem of occlusion and an improved edge detector based on the Canny edge operator is used to suppress spurious edges. This article concludes by quantifying the performance increase provided by the robust error measure and the robust edge detector. The performance of the algorithm is also demonstrated through the tracking of a sequence of cardiac MRI images.