Abstract
3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient's anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. We demonstrate our method with a felicitous application to the estimation of customized hearing aid devices.
Original language | English |
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Pages (from-to) | 47-56 |
Number of pages | 10 |
Journal | CAD Computer Aided Design |
Volume | 43 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2011 |
Externally published | Yes |
Funding
The authors thank Alex Zouhar at Siemens Corporate Research (SCR), Princeton NJ, USA for providing the hearing aid datasets. This work was supported by Siemens Corporate Research, Princeton NJ, USA .
Funders | Funder number |
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Siemens Corporate Research |
Keywords
- Customized shape modeling
- Hearing aid design
- Shape estimation
- Shape regression
- Statistical shape modeling