This dissertation proposes an efficient optimization approach for obtaining shape correspondence across a group of objects for statistical shape modeling. With each shape represented in a B-spline based parametric form, the correspondence across the shape population is cast as an issue of seeking a reparametrization for each shape so that a quality measure of the resulting shape correspondence across the group is optimized. The quality measure is the description length of covariance matrix of the shape population, with landmarks sampled on each shape. The movement of landmarks on each B-spline shape is controlled by the reparameterization of the B-spline shape. A gradient-based optimization approach is developed, including techniques such as constraint aggregation and adjoint senstivity for efficient, direct diffeomorphic reparameterization of landmarks. Numerical experiments on both synthetic and real 2D and 3D data sets demonstrate the efficiency and effectiveness of the proposed approach.
|Commitee:||Agam, Gady, Cassel, Kevin W., Nair, Sudhakar E., Pervan, Boris S., Qian, Xiaoping|
|School:||Illinois Institute of Technology|
|Department:||Mechanical, Materials and Aerospace Engineering|
|School Location:||United States -- Illinois|
|Source:||DAI-B 76/12(E), Dissertation Abstracts International|
|Subjects:||Engineering, Mechanical engineering, Computer science|
|Keywords:||Adjoint method, B-spline, Shape correspondence, Statistical shape model|
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