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- R. Vollgraf, M. Scholz,
I. Meinertzhagen, and K. Obermayer. Nonlinear Filtering of Electron
Micrographs by Means of Support Vector Regression.
.
In Advances in Neural Information Processing Systems 16, pages
717-724, Cambridge, Massachusetts, 2004. MIT Press.
(FTP PDF, 451 kb)
Nonlinear filtering can solve very complex problems, but typically
involve very time consuming calculations. Here we show that for filters that
are constructed as a RBF network with Gaussian basis functions, a
decomposition into linear filters exists, which can be computed efficiently
in the frequency domain, yielding dramatic improvement in speed. We present
an application of this idea to image processing. In electron micrograph
images of photoreceptor terminals of the fruit fly, Drosophila, synaptic
vesicles containing neurotransmitter should be detected and labeled
automatically. We use hand labels, provided by human experts, to learn a RBF
filter using Support Vector Regression with Gaussian kernels. We will show
that the resulting nonlinear filter solves the task to a degree of accuracy,
which is close to what can be achieved by human experts. This allows the very
time consuming task of data evaluation to be done efficiently.
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