Automatic landmark generation for Point Distribution Models
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Shape versus Size: Improved Understanding of the Morphology of Brain Structures
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Boundary Finding with Correspondence Using Statistical Shape Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Statistics of shape via principal geodesic analysis on lie groups
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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The essential goal for Statistical Shape Model (SSM) is to describe and extract the shape variations from the landmarks cloud. A standard technique for such variation extraction is by using Principal Component Analysis (PCA). However, PCA assumes that variations are linear in Euclidean vector space, which is not true or insufficient on many medical data. Therefore, we developed a new Geodesic Active Shape (GAS) mode by using Principal Geodesic Analysis (PGA) as an alternative of PCA. The new GAS model is combined with Minimum Description Length approach to find correspondence points across datasets automatically. The results are compared between original MDL and our proposed GAS MDL approach by using the measure of Specificity. Our preliminary results showed that our proposed GAS model achieved better scores on both datasets. Therefore, we conclude that our GAS model can capture shape variations reasonably more specifically than the original Active Shape Model (ASM). Further, analysis on the study of facial profiles dataset showed that our GAS model did not encounter the so-called "Pile Up" problem, whereas original MDL did.