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GECCO
2004
Springer
109views Optimization» more  GECCO 2004»
14 years 5 months ago
Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling
Abstract. When direct measurement of model parameters is not possible, these need to be inferred indirectly from calibration data. To solve this inverse problem, an algorithm that ...
Pedro J. Ballester, Jonathan N. Carter
AIPR
2005
IEEE
14 years 5 months ago
Discretization Error Based Mesh Generation for Diffuse Optical Tomography
In this paper, we analyze the perturbation in the reconstructed optical absorption images, resulting from the discretization of the forward and inverse problems. We show that the ...
Murat Guven, Birsen Yazici, Kiwoon Kwon, Eldar Gil...
ISBI
2007
IEEE
14 years 6 months ago
Fast Image Reconstruction Methods for Fully 3d Multispectral Optical Bioluminescence Tomography
We investigate fast iterative image reconstruction methods for fully 3D multispectral optical bioluminescence tomography where inhomogeneous optical properties are modeled using t...
Sangtae Ahn, Abhijit J. Chaudhari, Felix Darvas, C...
ISBI
2007
IEEE
14 years 6 months ago
Space-Time Sparsity Regularization for the Magnetoencephalography Inverse Problem
The concept of “Space-Time Sparsity” (STS) penalization is introduced for solving the magnetoencephalography (MEG) inverse problem. The STS approach assumes that events of int...
Andrew K. Bolstad, Barry D. Van Veen, Robert D. No...
ICASSP
2008
IEEE
14 years 6 months ago
Insights into the stable recovery of sparse solutions in overcomplete representations using network information theory
In this paper, we examine the problem of overcomplete representations and provide new insights into the problem of stable recovery of sparse solutions in noisy environments. We es...
Yuzhe Jin, Bhaskar D. Rao
ISBI
2009
IEEE
14 years 7 months ago
Improving M/EEG Source Localization with an Inter-Condition Sparse Prior
The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ2 norm....
Alexandre Gramfort, Matthieu Kowalski
ISBI
2008
IEEE
15 years 24 days ago
A spline-based forward model for Optical Diffuse Tomography
Reconstruction algorithms for Optical Diffuse Tomography (ODT) rely heavily on fast and accurate forward models. Arbitrary geometries and boundary conditions need to be handled ri...
Jean-Charles Baritaux, S. Chandra Sekhar, Michael ...
MICCAI
2007
Springer
15 years 1 months ago
Towards an Identification of Tumor Growth Parameters from Time Series of Images
In cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. In this article, we present a novel method for quantifyin...
Ender Konukoglu, Olivier Clatz, Pierre-Yves Bondia...
ICIP
2009
IEEE
15 years 1 months ago
Super-resolution With Continuous Scan Shift
Super-resolution methods aimed to restore the spectrum of an original image above the half sampling frequency. The restoration problem is generally viewed as an inverse problem an...
ICIP
2003
IEEE
15 years 1 months ago
An adaptive multigrid algorithm for region of interest diffuse optical tomography
Dueto diffuse nature of lightphotons, Diffuse Optical Tomography (DOT) image reconstruction is a challenging 3D problem with a relatively large number of unknowns and limited meas...
Murat Guven, Birsen Yazici, Xavier Intes, Britton ...