A method for quantitative evaluation of statistical shape models using morphometry
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
3D point correspondence by minimum description length in feature space
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Metrics, connections, and correspondence: the setting for groupwise shape analysis
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Minimization of intra-operative shaping of orthopaedic fixation plates: a population-based design
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Automatic construction of statistical shape models for vertebrae
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Pre-organizing Shape Instances for Landmark-Based Shape Correspondence
International Journal of Computer Vision
Simultaneous segmentation and correspondence establishment for statistical shape models
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
Generic fitted shapes (GFS): Volumetric object segmentation in service robotics
Robotics and Autonomous Systems
Ranking Star-Shaped Valued Mappings with Respect to Shape Variability
Journal of Mathematical Imaging and Vision
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Deformable shape models have wide application in computer vision and biomedical image analysis. This book addresses a key issue in shape modelling: establishment of a meaningful correspondence between a set of shapes. Correspondence has traditionally been established by manual annotation of the data, however this is a time-consuming and error-prone process that does not extend easily to 3D. Thus, almost all applications of statistical shape models have been restricted to 2D objects. This book explores a particular approach to establishing correspondence that casts model-building as an optimisation problem. A general framework is established to find the correspondence that optimises an objective function of model quality. Efficient methods, including implementation details, are presented for manipulating correspondences and for optimising the objective function. Practical example applications are presented for 2D as well as for 3D sets of shapes.