This work is dedicated to the analysis of the forward and the inverse problem to obtain a better approximation to the Electrical Impedance Tomography equation. In this case, we employ for the forward problem the numerical method based on the Taylor series in formal power and for the inverse problem the Finite Element Method.
For the analysis of the forward problem, we proposed a novel algorithm, which employs a regularization technique for the stability, additionally the parallel computing is used to obtain the solution faster; this modification permits to obtain an efficient solution of the forward problem. Then, the found solution is used in the inverse problem for the approximation employing the Finite Element Method.
The algorithms employed in this work are developed in structural programming paradigm in C++, including parallel processing; the time run analysis is performed only in the forward problem because the Finite Element Method due to their high recursive does not accept parallelism.
Some examples are performed for this analysis, in which several conductivity functions are employed for two different cases: for the analytical cases: the exponential and sinusoidal functions are used, and for the geometrical cases the circle at center and five disk structure are revised as conductivity functions. The Lebesgue measure is used as metric for error estimation in the forward problem, meanwhile, in the inverse problem PSNR, SSIM, MSE criteria are applied, to determine the convergence of both methods.
KEYWORDS: Image segmentation, Digital signal processing, Video acceleration, Video, Image processing, Simulink, 3D image processing, Data conversion, MATLAB, Image processing algorithms and systems
Conversion of available 2D data for release in 3D content is a hot topic for providers and for success of the 3D
applications, in general. It naturally completely relies on virtual view synthesis of a second view given by original 2D
video. Disparity map (DM) estimation is a central task in 3D generation but still follows a very difficult problem for
rendering novel images precisely. There exist different approaches in DM reconstruction, among them manually and
semiautomatic methods that can produce high quality DMs but they demonstrate hard time consuming and are
computationally expensive. In this paper, several hardware implementations of designed frameworks for an automatic
3D color video generation based on 2D real video sequence are proposed. The novel framework includes simultaneous
processing of stereo pairs using the following blocks: CIE L*a*b* color space conversions, stereo matching via
pyramidal scheme, color segmentation by k-means on an a*b* color plane, and adaptive post-filtering, DM estimation
using stereo matching between left and right images (or neighboring frames in a video), adaptive post-filtering, and
finally, the anaglyph 3D scene generation. Novel technique has been implemented on DSP TMS320DM648, Matlab’s
Simulink module over a PC with Windows 7, and using graphic card (NVIDIA Quadro K2000) demonstrating that the
proposed approach can be applied in real-time processing mode. The time values needed, mean Similarity Structural
Index Measure (SSIM) and Bad Matching Pixels (B) values for different hardware implementations (GPU, Single CPU,
and DSP) are exposed in this paper.
Different hardware implementations of designed automatic 2D to 3D video color conversion employing 2D video sequence are presented. The analyzed framework includes together processing of neighboring frames using the following blocks: CIELa*b* color space conversion, wavelet transform, edge detection using HF wavelet sub-bands (HF, LH and HH), color segmentation via k-means on a*b* color plane, up-sampling, disparity map (DM) estimation, adaptive postfiltering, and finally, the anaglyph 3D scene generation. During edge detection, the Donoho threshold is computed, then each sub-band is binarized according to a threshold chosen and finally the thresholding image is formed. DM estimation is performed in the following matter: in left stereo image (or frame), a window with varying sizes is used according to the information obtained from binarized sub-band image, distinguishing different texture areas into LL sub-band image. The stereo matching is performed between two (left and right) LL sub-band images using processing with different window sizes. Upsampling procedure is employed in order to obtain the enhanced DM. Adaptive post-processing procedure is based on median filter and k-means segmentation in a*b* color plane. The SSIM and QBP criteria are applied in order to compare the performance of the proposed framework against other disparity map computation techniques. The designed technique has been implemented on DSP TMS320DM648, Matlab’s Simulink module over a PC with Windows 7 and using graphic card (NVIDIA Quadro K2000) demonstrating that the proposed approach can be applied in real-time processing mode.
KEYWORDS: Digital signal processing, Video, Reconstruction algorithms, Video processing, 3D image processing, 3D image reconstruction, Wavelets, Image processing, Image compression, Optical flow
A novel approach for 3D image and video reconstruction is proposed and implemented. This is based on the wavelet
atomic functions (WAF) that have demonstrated better approximation properties in different processing problems in
comparison with classical wavelets. Disparity maps using WAF are formed, and then they are employed in order to
present 3D visualization using color anaglyphs. Additionally, the compression via Pth law is performed to improve the
disparity map quality. Other approaches such as optical flow and stereo matching algorithm are also implemented as
the comparative approaches. Numerous simulation results have justified the efficiency of the novel framework. The
implementation of the proposed algorithm on the Texas Instruments DSP TMS320DM642 permits to demonstrate
possible real time processing mode during 3D video reconstruction for images and video sequences.
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