Geometric harmonics as a statistical image processing tool for images defined on irregularly-shaped domains, in Proceedings of 13th IEEE Statistical Signal Processing Workshop, pp. 425-430, 2005.

Abstract

We propose a new method to analyze and represent stochastic data recorded on a domain of general shape by computing the eigenfunctions of Laplacian defined over there (also called "geometric harmonics") and expanding the data into these eigenfunctions. In essence, what our Laplacian eigenfunctions do for data on a general domain is roughly equivalent to what the Fourier cosine basis functions do for data on a rectangular domain. Instead of directly solving the Laplacian eigenvalue problem on such a domain (which can be quite complicated and costly), we find the integral operator commuting with the Laplacian and then diagonalize that operator. We then show that our method is better suited for small sample data than the Karhunen-Loeve transform. In fact, our Laplacian eigenfunctions depend only on the shape of the domain, not the statistics (e.g., covariance) of the data. We also discuss possible approaches to reduce the computational burden of the eigenfunction computation.

  • Get the full paper (corrected version as of 01/19/07): PDF file.
  • Get the official version (older than the above) via doi:10.1109/SSP.2005.1628633.
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