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Monday, September 26, 2011 - 14:00 ,
Location: Skiles 006 ,
Xiaojing Ye ,
School of Mathematics, Georgia Tech ,
Organizer: Haomin Zhou

We will discuss the recent developments of fast image reconstrcution with total variation (TV) regularization whose robustness has been justfied by the theory of compressed sensing. However, the solution of TV based reconstruction encounters two main difficulties on the computational aspect of many applications: the inversion matrix can be large, irregular, and severely ill-conditioned, and the objective is nonsmooth. We introduce two algorithms that tackle the problem using variable splitting and optimized step size selection. The algorithms also provide a general framework for solving large and ill-conditioned linear inversion problem with TV regularization. An important and successful application of TV based image reconstruction in magnetic resonance imaging (MRI) known as paratially parallel imaging (PPI) will be discussed. The numerical results demonstrate significantly improved efficiency and accuracy over the state-of-the-arts.

Monday, September 19, 2011 - 14:05 ,
Location: Skiles 006 ,
Jeff Geronimo ,
School of Mathematics, Georgia Tech ,
Organizer:

Using the technique of intertwining multiresolution analysis piecewise
linear, continuous, orthogonal, wavelets on a regular hexagon are
constructed. We will review the technique of intertwining multiresolution
analysis in the one variable case then indicate the modifications necessary
for the two variable construction. This is work with George Donovan and Doug
Hardin.

Monday, September 12, 2011 - 14:00 ,
Location: Skiles 006 ,
Rick Chartrand ,
Los Alamos National Laboratory, Theoretical Division ,
Organizer: Haomin Zhou

There has been much recent work applying splitting algorithms to optimization problems designed to produce sparse solutions. In this talk, we'll look at extensions of these methods to the nonconvex case, motivated by results in compressive sensing showing that nonconvex optimization can recover signals from many fewer measurements than convex optimization. Our examples of the application of these methods will include image reconstruction from few measurements, and the decomposition of high-dimensional datasets, most notably video, into low-dimensional and sparse components.

Monday, April 18, 2011 - 14:00 ,
Location: Skiles 005 ,
JungHa An ,
California State University, Stanislaus ,
Organizer: Sung Ha Kang

Medical imaging is the application of mathematical and engineering models to create images of the human body for clinical purposes or medical science by using a medical device. One of the main objectives of medical imaging research is to find the boundary of the region of the interest. The procedure to find the boundary of the region of the interest is called a segmentation. The purpose of this talk is to present a variational region based algorithm that is able to deal with spatial perturbations of the image intensity directly. Image segmentation is obtained by using a Gamma-Convergence approximation for a multi-scale piecewise smooth model. This model overcomes the limitations of global region models while avoiding the high sensitivity of local approaches. The proposed model is implemented efficiently using recursive Gaussian convolutions. The model is applied to magnetic resonance (MR) images where image quality depends highly on the acquisition protocol. Numerical experiments on 2-dimensional human liver MR images show that our model compares favorably to existing methods.This work is done in collaborated with Mikael Rousson and Chenyang Xu.

Friday, April 15, 2011 - 14:00 ,
Location: Skiles 005 ,
Xun Jia ,
University of California, San Diego, Department of Radiation Oncology ,
Organizer: Sung Ha Kang

Cone Beam Computer tomography (CBCT) has been broadly applied incancer radiation therapy, mainly for positioning patients to align withtreatment radiation beams. As opposed to tomography reconstruction problemsfor diagnostic purposes, CBCT reconstruction in radiotherapy requires a highcomputational efficiency, since it is performed while patient is lying on acouch, waiting for the treatment. Moreover, the excessive radiation dosefrom frequent scans has become a clinical concern. It is therefore desirableto develop new techniques to reconstruct CBCT images from low dose scans. Inthis talk, I will present our recent work on an iterative low dose CBCTreconstruction technique via total variation regularization and tight frameregularization. It is found that 40~60 x-ray projections are sufficient toreconstruct a volumetric image with satisfactory quality in about 2min. Wehave also studied 4 dimensional CBCT (4DCBCT) reconstruction problem viatemporal non-local means (TNLM) and high quality 4DCBCT images can beobtained. Our algorithms have been fully implemented on a graphicsprocessing unit. Detailed implementation techniques will also be addressed.

Monday, April 11, 2011 - 14:00 ,
Location: Skiles 005 ,
Alex Alexeev ,
Georgia Tech Mechanical Engineering ,
Organizer:

Biomimetic synthetic cilia can be
effectively utilized for regulating microscale transport processes at
interfaces. Using computer simulations, we examine how polymeric cilia can be
harnessed to control the motion of microscopic particles suspended in a viscous
fluid. The cilia are modeled as deformable, elastic filaments and our
simulations capture the complex fluid-structure interactions among these
filaments, channel walls and surrounding solution. We show that non-motile
cilia that are tilted with respect to the surface can hydrodynamically
direct solid particles towards channel walls, thereby, inducing their rapid
deposition. When synthetic cilia are actuated by a
sinusoidal force that is applied at the free ends, the beating cilia can
either drive particles downwards toward the substrate or expelled particles into
the fluid above the actuated cilial layer. This dynamic behavior can be
regulated by changing the driving frequency. The findings uncover new routes
for controlling the deposition of microscopic particles in microfluidic
devices.

Monday, April 4, 2011 - 14:00 ,
Location: Skiles 005 ,
Lili Ju ,
Department of Mathematics, University of South Carolina ,
Organizer: Haomin Zhou

In this talk, we present a parallel finite element implementation ontetrahedral grids of the nonlinear three-dimensional nonlinear Stokes model for thedynamics and evolution of ice-sheets. Discretization is based on a high-orderaccurate scheme using the Taylor-Hood element pair. Both no-slip and sliding boundary conditions at the ice-bedrock boundary are studied. In addition, effective solvers using preconditioning techniques for the saddle-point system resulting fromthe discretization are discussed and implemented. We demonstrate throughestablished ice-sheet benchmark experiments that our finite element nonlinear Stokesmodel performs at least as well as other published and established Stokes modelsin the field, and the parallel solver is shown to be efficient, robust, and scalable.

Monday, March 28, 2011 - 14:00 ,
Location: Skiles 005 ,
Yifei Lou ,
GaTech ECE (Minerva Research Group) ,
Organizer: Sung Ha Kang

Image restoration has been an active research topic in imageprocessing and computer vision. There are vast of literature, mostof which rely on the regularization, or prior information of theunderlying image. In this work, we examine three types of methodsranging from local, nonlocal to global with various applications.A classical approach for local regularization term is achieved bymanipulating the derivatives. We adopt the idea in the localpatch-based sparse representation to present a deblurringalgorithm. The key observation is that the sparse coefficientsthat encode a given image with respect to an over-complete basisare the same that encode a blurred version of the image withrespect to a modified basis. Following an``analysis-by-synthesis'' approach, an explicit generative modelis used to compute a sparse representation of the blurred image,and its coefficients are used to combine elements of the originalbasis to yield a restored image.We follows the framework that generates the neighborhood filtersto an variational formulation for general image reconstructionproblems. Specifically, two extensions regarding to the weightcomputation are investigated. One is to exploit the recurrence ofstructures at different locations, orientations and scales in animage. While previous methods based on ``nonlocal filtering'' identify corresponding patches only up to translations, we consider more general similarity transformation.The second algorithm utilizes a preprocessed data as input for theweight computation. The requirements for preprocessing are (1) fastand (2) containing sharp edges. We get superior results in theapplications of image deconvolution and tomographic reconstruction.A Global approach is explored in a particular scenario, that is,taking a burst of photographs under low light conditions with ahand-held camera. Since each image of the burst is sharp but noisy,our goal is to efficiently denoise these multiple images. Theproposed algorithm is a complex chain involving accurateregistration, video equalization, noise estimation and the use ofstate-of-the-art denoising methods. Yet, we show that this complexchain may become risk free thanks to a key feature: the noise modelcan be estimated accurately from the image burst.

Monday, March 14, 2011 - 14:00 ,
Location: Skiles 005 ,
John Schotland ,
University of Michigan, Ann Arbor ,
Organizer: Haomin Zhou

The inverse problem of optical tomography consists of recovering thespatially-varying absorption of a highly-scattering medium from boundarymeasurements. In this talk we will discuss direct reconstruction methods forthis problem that are based on inversion of the Born series. In previouswork we have utilized such series expansions as tools to develop fast imagereconstruction algorithms. Here we characterize their convergence, stabilityand approximation error. Analogous results for the Calderon problem ofreconstructing the conductivity in electrical impedance tomography will alsobe presented.

Monday, March 7, 2011 - 14:00 ,
Location: Skiles 005 ,
Darshan Bryner ,
Naval Surface Warfare Center/FSU ,
Organizer: Haomin Zhou

There are several definitions of
the word shape; of these, the most
important to this research is “the external form or appearance of someone or
something as produced by its outline.”
Shape Analysis in this context focuses specifically on the mathematical
study of explicit, parameterized curves in 2D obtained from the boundaries of
targets of interest in Synthetic Aperture Sonar (SAS) imagery. We represent these curves with a
special “square-root velocity function,” whereby the space of all such
functions is a nonlinear Riemannian manifold under the standard L^2
metric. With this curve
representation, we form the mathematical space called “shape space” where a
shape is considered to be the orbit of an equivalence class under the group
actions of scaling, translation, rotation, and re-parameterization. It is in this quotient space that we can
quantify the distance between two shapes, cluster similar shapes into classes,
and form means and covariances of shape classes for statistical
inferences. In this particular
research application, I use this shape analysis framework to form probability
density functions on sonar target shape classes for use as a shape prior energy
term in a Bayesian Active Contour model for boundary extraction in SAS images. Boundary detection algorithms generally
perform poorly on sonar imagery due to their typically low signal to noise
ratio, high speckle noise, and muddled or occluded target edges; thus, it is
necessary that we use prior shape information in the evolution of an active
contour to achieve convergence to a meaningful target boundary.