Surfaces in early range image understanding
Surfaces in early range image understanding
Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Object Recognition Using Bipartite Matching Embedded in Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Object Recognition from Range Images
Three-Dimensional Object Recognition from Range Images
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Craniofacial Surgery Simulation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Rigid Point-Surface Registration Using an EM Variant of ICP for Computer Guided Oral Implantology
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Brain Symmetry Plane Computation in MR Images Using Inertia Axes and Optimization
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Surface Reconstruction for Computer Vision-Based Craniofacial Surgery
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Hi-index | 0.00 |
The paper addresses the problem of virtual craniofacial reconstruction from a set of Computer Tomography (CT) images, with the multiple objectives of achieving accurate local matching of the opposable fracture surfaces and preservation of the global shape symmetry and the biomechanical stability of the reconstructed mandible. The first phase of the reconstruction, with the mean squared error as the performance metric, achieves the best possible local surface matching using the Iterative Closest Point (ICP) algorithm and the Data Aligned Rigidity Constrained Exhaustive Search (DARCES) algorithm each used individually and then in a synergistic combination. The second phase, which consists of an angular perturbation scheme, optimizes a composite reconstruction metric. The composite reconstruction metric is a linear combination of the mean squared error, a global shape symmetry term and the surface area which is shown to be a measure of biomechanical stability. Experimental results, including a thorough validation scheme on simulated fractures in phantoms of the craniofacial skeleton, are presented.