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Probability Models for Joint Row Column Clustering and High Dynamic Range Image Processing
Chris Pal
UMass
Abstract
Probability models and their graphical descriptions provide a
principled way of constructing models for data. As well, graphical
models aid with the visualization and development of algorithms for
optimizing probability models. In this talk I present two information
processing problems inspired by cDNA microarray experiments, their
processing and general image processing. The models I present are
described by graphical models with similar structures and this
facilitates the development of similar algorithms for optimizing the
models. First, I present a model for jointly clustering the rows and
columns of a cDNA microarray data matrix, allowing such a matrix to be
permuted into an approximately block constant form. I present a new
algorithm that proves to be superior to many other alternatives based
on synthetic data experiments and real data experiments for the task
of determining subsets of co-expressed genes. Second, I present a new
method for combining images taken with different camera settings into
a higher dynamic range image using only pixel values. I derive priors
for imaging functions using a generative model of the derivative
structure of functions and tie together generative models and
smoothness regularization. I derive a new optimization technique to
estimate both functions and high dynamic range irradiance estimates.
Finally, I present some compelling image processing results expanding
the dynamic range of digital imagery.
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