Abstract
Here we provide a method for comparing geometric objects. Two objects of interest are embedded into an infinite dimensional Hilbert space using their Laplacian eigenvalues and eigenfunctions, truncated to a finite dimensional Euclidean space, where correspondences between the objects are searched for and voted on. To simplify correspondence finding, we propose using several geometric invariants to reduce the necessary computations. This method improves on voting methods by identifying isometric regions including shapes of genus greater than 0 and dimension greater than 3, as well as almost retaining isometry.
Advisor
Ke Zhu
Committee Member
Wook Kim
Committee Member
Brandon Rowekamp
Date of Degree
2020
Language
english
Document Type
Thesis
Degree
Master of Arts (MA)
Department
Mathematics and Statistics
College
Science, Engineering and Technology
Recommended Citation
Harr, A. (2020). Heat kernel voting with geometric invariants [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1021/
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