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ACM SIGGRAPH 2005 Papers
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ACM SIGGRAPH 2006 Papers
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ACM SIGGRAPH 2006 Papers
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2008 papers
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Optimization of Surface Registrations Using Beltrami Holomorphic Flow
Journal of Scientific Computing
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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IEEE Computer Graphics and Applications
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International Journal of Computer Vision
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This paper presents a novel approach based on the shape space concept to classify deformations of 3D models. A new quasi-conformal metric is introduced which measures the curvature changes at each vertex of each pose during the deformation. The shapes with similar deformation patterns follow a similar deformation curve in shape space. Energy functional of the deformation curve is minimized to calculate the geodesic curve connecting two shapes on the shape space manifold. The geodesic distance illustrates the similarity between two shapes, which is used to compute the similarity between the deformations. We applied our method to classify the left ventricle deformations of myopathic and control subjects, and the sensitivity and specificity of our method were 88.8% and 85.7%, which are higher than other methods based on the left ventricle cavity, which shows our method can quantify the similarity and disparity of the left ventricle motion well.