Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Active shape models—their training and application
Computer Vision and Image Understanding
Generation of point-based 3D statistical shape models for anatomical objects
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Development of 3D Measuring Techniques for the Analysis of Facial Soft Tissue Change
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Constructing Dense Correspondences to Analyze 3D Facial Change
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Measures for Benchmarking of Automatic Correspondence Algorithms
Journal of Mathematical Imaging and Vision
Determining discriminative anatomical point pairings using adaboost for 3D face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multi-modal ear and face modeling and recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper, we present a method for constructing dense correspondences between 3D open surfaces that is sufficiently accurate to permit clinical analysis of 3D facial morphology. Constructing dense correspondences between 3D models representing facial surface anatomy is a natural extension of landmark-based methods for analysing facial shape or shape changes. Compared to landmark-based methods, dense correspondences sample the entire surface and hence provide a more thorough description of the underlying 3D structures. The method we present here is based on elastic deformation, which deforms a 3D generic model onto the 3D surface of a specific individual. We are then able to construct dense correspondences between different individuals by analysing their corresponding deformed generic models. Validation experiments show that, using only five manually placed landmarks, approximately 95% of triangles on the deformed generic mesh model are within the range of +/-0.5mm to the corresponding original model. The established dense correspondences have been exploited within a principal components analysis (PCA)-based procedure for comparing the facial morphology of a control group to that of a surgically managed group comprising the patients who have been subject to facial lip repair.