Liu Jian-xin

Automatic Color Image Segmentation Based on Anisotropic Diffusion and the Application in Cancer Cell Segmentation

This paper presents a new algorithm using anisotropic diffusion and mean shift applied in the color image. It is shown how segmentation can benefit from splitting color signals into chromatic and achromatic channels and separately smoothing them through anisotropic diffusion. The result of diffusion is segmented by mean shift techniques and their combination yields the final image partition into homogeneous regions. Some experimental results applied to cancer cell images are reported in this paper which verifies the effectiveness of the proposed technique.

Ground penetating radar imaging algorithm of targets located in unknown subsurface environment based on compressive sensing

A new GPR(ground penetrating radar) imaging algorithm based on CS(compressive sensing) technique is presented in this paper. It aims at imaging of targets located in unknown subsurface environment. Firstly, a compound objective vector is constructed, including both unknown targets vector and unknown dielectric parameter of background environment. Secondly, the scattering and imaging model is formed based on GPR electromagnetic scattering principle and CS framework. Thirdly, the compound objective vector is obtained by solving a convex optimization problem. Experimental results of GPR simulation data show the effectiveness of this algorithm.

Application condition of linearized model uncertainty estimate

This paper studies the application condition of linearized model uncertainty estimate numerically for the Magnetotelluric (MT) problem using Bayesian inference theory, basedX on a three layer case with different parameter combinations. The nonlinear uncertainty estimate is calculated with relatively intensive computation, via Markov chain Monte Carlo method of Metropolis Hastings, giving an unbiased sampling from the posterior probability density (PPD), while the linearized uncertainty estimate which can be achieved cheaply, is based on the Gaussian distribution centered at the optimal model calculated based on a nonlinear optimization method, adaptive simplex simulated annealing (ASSA). The results show that linear uncertainty estimate can be evaluated as an alternative to the nonlinear uncertainty estimate for certain parameter combinations (model structure).

Numerical simulation of 2-D MT tipper response using finite element method

In the two-dimensional geo-electric model, the tipper response of magnetotelluric(MT) depends on the numerical simulation TE mode. Firstly, the boundary value problem and variational problem were shown, and the finite element method was used for element subdivision, interpolation, integration and general synthesis. Secondly, the electric field value of each node was obtained through solving the complex equation. Lastly, the partial derivatives along the horizontal and vertical electric field obtained using the difference method, and the tipper response of the two-dimensional MT can be calculated. By the numerical simulation of the two models, the tipper responses are obtained including real part, imaginary part and amplitude, and the results reflect the horizontal distribution of the body.

Two-dimensional diffraction tomography algorithm of underground objects located in planar multilayer medium 1

An efficient inverse scattering algorithm is developed to reconstruct the permittivity profile of two-dimensional(2-D) dielectric objects buried in planar multilayer environment. The forward model of this inversion scheme is based upon first order Born approximation and 2-D planar multilayer scalar Green function. A planar multilayer diffraction tomography algorithm is established subsequently. Numerical results are provided to illustrate the performance of the inversion scheme.