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bioimage informatics lab |
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Research The
main focus in our lab is on building automated systems for processing and
interpretation of biological images. To that end, we use both the tools
already developed in signal and image processing and machine learning as well
as develop new tools specifically tailored to the problem at hand. |
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Current
research areas We focus on developing
automated systems for analysis and interpretation of biological images. Our
current work focuses on efficient acquisition of fluorescence microscopy data
sets, segmentation of such data sets as well as classification of protein
subcellular location patterns. Frames are redundant
representations which have become popular in recent years. Work here focuses
on characterizing finite-dimensional frames, searching for useful frame
families as well as applying frames to biomolecular
and cellular imaging and biometrics. This area was started by
Markus Püschel, whose goal is to formulate an algebraic framework for
signal processing. Our current work focuses on understanding and formulating
such a framework for filter banks and multiresolution transforms. |
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Past
research areas Correlation, as a pattern
recognition tool, may be applied to texture features that have joint locality
in space and frequency. Wavelets produce these types of discriminatory
features, and we can prune wavelet packets to recover the best subspaces for
correlation filter recognition. Multiple descriptions This is a technique where the
data is broken into several streams with some redundancy among the streams.
When all the streams are received, one can guarantee low distortion at the
expense of having a slightly higher bit rate than a system designed purely
for compression. On the other hand, when only some of the streams are
received, the quality of the reconstruction degrades gracefully, which is
very unlikely to happen with a system designed purely for compression. MD filter banks and wavelets One of the first works on
multidimensional filter banks and associated wavelet bases including the
first examples of a regular irreducible two-dimensional wavelet as well as an
orthonormal and symmetric two-dimensional wavelet basis. There is also a
brief description on building local orthogonal bases in multiple dimensions
as well as assorted applications such as HDTV representation and coding, use
of three-dimensional filter banks with the FCO lattice and deinterlacing by successive
approximation. Local orthogonal bases Local cosine bases, or, MDCT,
have been shown to be very useful in audio and image coding. Some video works
contain local cosine bases as well. For that reason, I investigated the local
cosine bases in two dimensions. Moreover, a general framework was put into
place leading to local orthogonal bases usable for audio, image and video
coding. Arbitrary tilings One of the main goals of
signal analysis in recent years has been to develop a mixed signal
representation in terms of some elementary blocks well localized in time and
frequency, where these blocks are known as time-frequency atoms. Each one of
these blocks would reside mostly in a well-defined area (usually a rectangle)
in the time-frequency plane. We discuss here several ways of building these
arbitrary tilings, with particular emphasis on those obtainable from local
orthogonal bases. Nonuniform filter banks The most studied case of
filter banks is the one with integer sampling factors. However, if one wants
to analyze the signal into unequal subbands (such
as in acoustics), rational sampling factors have to be allowed. You can find
here about two solutions to the open problem of constructing nonuniform filter banks. The first is general while the
second one is based on local orthogonal bases. As a result of the second one,
we were able to build a critical-band filter bank for use in audio coding. Quantization analysis This work was motivated by
the need to obtain even more powerful compression schemes than what is
currently available. For subband systems, unsolved problems with large
potential benefits are in the area of joint design of quantization and
filtering. The work here is one of the few on the subject. Texture work We propose a perceptually-based
system for pattern retrieval and matching. We detect basic visual categories
that people use in judgment of similarity, and design a computational model
which accepts patterns as input, and depending on the query, produces a set
of choices that follow human behavior in pattern matching. Graphics work We propose an efficient
simplification method for regular meshes obtained with a binary subdivision
scheme. Our mesh connectivity is constrained with a quadtree data structure.
We propose a quadtree built especially for this class of meshes having a
constant-time traversal property. We introduce a rate-distortion (RD)
framework to decimate the mesh and build a progressive representation for the
model. We apply our technique to a large dataset of terrains and give
extensive experimental results. |