3D Shape Reconstruction in Optical Tomography Using Spherical Harmonics and BEM

Zacharopoulos, Athanasios; Arridge, Simon; Dorn, Oliver; Kolehmainen, Ville; Sikora, J.

doi:10.1163/156939306779292165

Cited by 24 publications

(21 citation statements)

References 5 publications

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“…Because the main goal of this paper is to show that projected features of frequencydiversity RCS can well identify radar targets even though there exist measured noises. From physical points of views, the RCS based recognition of radar targets is basically an approximate approach of inverse scattering [12][13][14][15][16][17][18][19][20][21][22][23]. Since frequency-diversity techniques are successful in inverse scattering, they must contain much information about targets.…”

Section: Resultsmentioning

confidence: 99%

Radar Target Recognition by Projected Features of Frequency-Diversity RCS

Lee¹,

Huang²,

Fang³

2008

PIER

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Abstract-In this paper, the radar target recognition is given by projected features of frequency-diversity RCS (radar cross section). The frequency diversity means signals are collected by sweeping the frequency of the incident illumination. Initially, the frequencydiversity RCS data from targets are collected and projected onto the PCA (principal components analysis) space. The elementary recognition of targets is efficiently performed on the PCA space. To achieve well separate recognition of targets, the features of the PCA space are further projected onto the LDA (linear discriminant algorithm) space. Simulation results show that accurate results of radar target recognition can be obtained by the proposed frequencydiversity scheme. In addition, the proposed frequency-diversity scheme has good ability to tolerate noise effects in radar target recognition.

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Section: Resultsmentioning

confidence: 99%

Radar Target Recognition by Projected Features of Frequency-Diversity RCS

Lee¹,

Huang²,

Fang³

2008

PIER

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“…The inverse problem is formulated in terms of a minimization of errors based on the forward problem. Finite element method is well suited for the forward problem in 3D ECT, the application of boundary element method [24] could be potentially interesting, especially for shape reconstruction problem. We use low frequency approximation to the Maxwell's equations.…”

Section: Forward Modellingmentioning

confidence: 99%

Four-Dimensional Electrical Capacitance Tomography Imaging Using Experimental Data

Soleimani¹,

Mitchell²,

Banasiak³

et al. 2009

PIER

134

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Abstract-Electrical capacitance tomography (ECT) is a relatively mature non-invasive imaging technique that attempts to map dielectric permittivity of materials. ECT has become a promising monitoring technique in industrial process tomography especially in fast flow visualization. One of the most challenging tasks in further development of ECT for real applications are the computational aspects of the ECT imaging. Recently, 3D ECT has gained interest because of its potential to generate volumetric images. Computational time of image reconstruction in 3D ECT makes it more difficult for real time applications. In this paper, we present a robust and computationally efficient 4D image reconstruction algorithm applied to real ECT data. The method takes advantage of temporal correlation between 3D ECT frames to reconstruct movies 4D of dielectric maps. Image reconstruction results are presented for the proposed algorithms for experimental ECT data of a rapidly moving object.Corresponding author: M. Soleimani (m.soleimani@bath.ac.uk). 172Soleimani et al.

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“…In this method the regularized solution was obtained using a parameterized trust region approach to estimate the region of maximum curvature of the L-curve. Method proposed here can be used in other imaging techniques [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Trust Region Subproblem for 3d Electrical Impedance Tomography Inverse Problem Using Experimental Data

Goharian¹,

Soleimani²,

Moran³

2009

PIER

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Abstract-Image reconstruction in electrical impedance tomography (EIT) is an ill-posed nonlinear inverse problem. Regularization methods are needed to solve this problem. The results of the illposed EIT problem strongly depends on noise level in measured data as well as regularization parameter. In this paper, we present trust region subproblem (TRS), with the use of L-curve maximum curvature criteria to find a regularization parameter. Currently Krylov subspace methods especially conjugate gradient least squares (CGLS) are used for large scale 3D problem. CGLS is an efficient technique when the norm of measured noise is exactly known. This paper demonstrates that CGLS and TRS converge to the same point on the L-curve with the same noise level. TRS can be implemented efficiently for large scale inverse EIT problem as CGLS with no need a priori knowledge of the noise level.Corresponding author: M. Soleimani (m.soleimani@bath.ac.uk).

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3D Shape Reconstruction in Optical Tomography Using Spherical Harmonics and BEM

Cited by 24 publications

References 5 publications

Radar Target Recognition by Projected Features of Frequency-Diversity RCS

Radar Target Recognition by Projected Features of Frequency-Diversity RCS

Four-Dimensional Electrical Capacitance Tomography Imaging Using Experimental Data

A Trust Region Subproblem for 3d Electrical Impedance Tomography Inverse Problem Using Experimental Data

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