This paper presents our study on image reconstruction algorithms for THz near field scanning systems. Based on the principle of Physical Optics (PO) algorithm, we have proposed and investigated a novel transposed conv...
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ISBN:
(纸本)9781538627204
This paper presents our study on image reconstruction algorithms for THz near field scanning systems. Based on the principle of Physical Optics (PO) algorithm, we have proposed and investigated a novel transposed convolution imagereconstruction algorithm (TC), in comparison with the back propagation algorithm (BP) in simulation and experiment.
Integral Imaging is a technique that has the capability of providing not only the spatial, but also the angular information of three-dimensional (3D) scenes. Some important applications are the 3D display and digital ...
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ISBN:
(纸本)9781510601086
Integral Imaging is a technique that has the capability of providing not only the spatial, but also the angular information of three-dimensional (3D) scenes. Some important applications are the 3D display and digital post-processing as for example, depth-reconstruction from integral images. In this contribution we propose a new reconstruction method that takes into account the integral image and a simplified version of the impulse response function (IRF) of the integral imaging (InI) system to perform a two-dimensional (2D) deconvolution. The IRF of an InI system has a periodic structure that depends directly on the axial position of the object. Considering different periods of the IRFs we recover by deconvolution the depth information of the 3D scene. An advantage of our method is that it is possible to obtain nonconventional reconstructions by considering alternative synthetic impulse responses. Our experiments show the feasibility of the proposed method.
Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it...
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Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it is faced with the limitation of energy resources and the transmission bottleneck of sensors in WSN. In this paper, a Model-Based Adaptive Iterative Hard Thresholding (MAIHT) compressive sensing scheme is developed, where a large number of inexpensive sensors are used to collect fine-grained, real-time volcano-seismic signals while a small number of powerful coordinator nodes process and pick arrival times of primary waves (i.e., P-phases). The paper contribution is two-fold. Firstly, a sparse measurement matrix with theoretical analysis of its restricted isometry property (RIP) is designed to simplify the acquisition process, thereby reducing required storage space and computational demands in sensors. Secondly, a compressive sensing reconstruction algorithm with theoretical analysis of its error bound is presented. Experimental results based on real volcano-seismic data collected from a volcano show that our method can recover the original seismic signal and achieve accurate P-phase picking based on the reconstructed seismic signal.
Distributed camera networks play an important role in public security surveillance. Analyzing video sequences from cameras set at different angles will provide enhanced performance for detecting abnormal events. In th...
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Distributed camera networks play an important role in public security surveillance. Analyzing video sequences from cameras set at different angles will provide enhanced performance for detecting abnormal events. In this paper, an abnormal detection algorithm is proposed to identify unusual events captured by multiple cameras. The visual event is summarized and represented by the histogram of the optical flow orientation descriptor, and then a multikernel strategy that takes the multiview scenes into account is proposed to improve the detection accuracy. A nonlinear one-class SVM algorithm with the constructed kernel is then trained to detect abnormal frames of video sequences. We validate and evaluate the proposed method on the video surveillance dataset PETS.
Magnetic induction tomography (MIT) is a non-invasive and non-contact medical imaging technology. It is a visualized implementation based on image reconstruction algorithms, which plays an important role in determinin...
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ISBN:
(纸本)9781479958382
Magnetic induction tomography (MIT) is a non-invasive and non-contact medical imaging technology. It is a visualized implementation based on image reconstruction algorithms, which plays an important role in determining the image quality. In this paper, an improved back-projection algorithm based on magnetic field lines is presented and can be described as follows. Firstly, the phase data collected at the imaging region boundary coil is normalized. Secondly, the back-projection matrix is simply weighted based on the difference of back-projection paths and the sensitivity to conductivity change of the pixel in the imaging region. Finally, a data modification model is proposed based on the electromagnetic relation in MIT. The results show that the improved algorithm can accurately reflect the distribution of conductivity and the object position in the imaging region with a high resolution. Specifically, when the object is located at the boundary of the imaging region, the back-projection algorithm of MIT can improve the convergence of the edge of the detected object. The anti-noise performance of improved algorithm could meet the standard of applications. In a measurement system with SNR is over 20dB, images could be successfully reconstructed without being affected by the noise.
We report on a broadband multi-element THz imaging system based on fiber-coupled, integrated photoconductive emitters and detectors. 32 detectors and 32 emitters are arranged in a planar array. Advanced image reconstr...
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ISBN:
(纸本)9781424466573
We report on a broadband multi-element THz imaging system based on fiber-coupled, integrated photoconductive emitters and detectors. 32 detectors and 32 emitters are arranged in a planar array. Advanced image reconstruction algorithms are employed to reconstruct an object in the imaging plane.
Objective: To evaluate the effect of different imagereconstruction parameters on quantitative automated measurements of pulmonary emphysema in chest multidetector-row spiral computed tomography. Materials and Methods...
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Objective: To evaluate the effect of different imagereconstruction parameters on quantitative automated measurements of pulmonary emphysema in chest multidetector-row spiral computed tomography. Materials and Methods: Thirty patients with known emphysema underwent multidetector-row spiral computed tomography. Retrospective reconstruction with a soft tissue kernel (Siemens B20 at Imin. 2-mm, and 3-mm slices) and 4 alternative kernel grades (from smooth to sharp: Siemens 1330, B40, B50, B60 at 1-mm slices) was performed. Total lung volume, emphysema volume (EV), 15th percentile density, and 4 EV clusters were quantified. Results were compared with those of standard algorithm B20/1-mm slices. Results: Differences in total lung volume were less than 0.2%. Alternative kernel grades resulted in a significantly increased average EV The 15th percentile density showed a significant average difference for all alternative algorithms. The large emphysema cluster showed a significant change for reconstructionalgorithms 1350, 1360, B20/2 mm and B20/3 mm. Conclusions: Pulmonary EV is significantly affected by different reconstructionalgorithms.
We have worked on multi-dimensional magnetic resonance imaging (MRI) data acquisition and related imagereconstruction methods that aim at reducing the MRI scan time. To achieve this scan-time reduction we have combin...
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We have worked on multi-dimensional magnetic resonance imaging (MRI) data acquisition and related imagereconstruction methods that aim at reducing the MRI scan time. To achieve this scan-time reduction we have combined the approach of 'increasing the speed' of k-space acquisition with that of 'deliberately omitting' acquisition of k-space trajectories (sparse sampling). Today we have a whole range of (sparse) sampling distributions and related reconstruction methods. In the context of a European Union Training and Mobility of Researchers project we have decided to integrate all methods into one coordinating software system. This system meets the requirements that it is highly structured in an object-oriented manner using the Unified Modeling Language and the Java programming environment, that it uses the client-server approach, that it allows multi-client communication sessions with facilities for sharing data and that it is a true distributed computing system with guaranteed reliability using core activities of the Java Jini package. (C) 2002 Published by Elsevier Science B.V.
Recently we have developed a Java-based heterogeneous distributed computing system for the field of magnetic resonance imaging (MRI). It is a software system for embedding the various image reconstruction algorithms t...
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Recently we have developed a Java-based heterogeneous distributed computing system for the field of magnetic resonance imaging (MRI). It is a software system for embedding the various image reconstruction algorithms that we have created for handling MRI data sets with sparse sampling distributions. Since these data sets may result from multi-dimensional MRI measurements our system has to control the storage and manipulation of large amounts of data. In this paper we describe how we have employed the extensible markup language (XML) to realize this data handling in a highly structured way. To that end we have used Java packages, recently re eased by Sun Microsystems, to process XML documents and to compile pieces of XML code into Java classes. We have effectuated a flexible storage and manipulation approach for all kinds of data within the MRI system, such as data describing and containing multi-dimensional MRI measurements, data configuring imagereconstruction methods and data representing and visualizing the various services of the system. We have found that the object-oriented approach, possible with the Java programming environment, combined with the XML technology is a convenient way of describing and handling various data streams in heterogeneous distributed computing systems. (C) 2003 Elsevier Science Ireland Ltd. All rights reserved.
Enhanced contrast of frequency-domain photon migration (FDPM) measurements for successful biomedical optical imaging may be theoretically achieved with exogenous fluorescence contrast agents. However in practice, the ...
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Enhanced contrast of frequency-domain photon migration (FDPM) measurements for successful biomedical optical imaging may be theoretically achieved with exogenous fluorescence contrast agents. However in practice, the reduced fluorescence signals detected at the air-tissue interface possess significant noise when compared to the signals collected at the incident wavelength. In this study, we experimentally assess signal to noise ratios (SNRs) for FDPM measurements in homogeneous tissue-like scattering media which absorb and fluorescence. At 100 MHz, the SNR for our single-pixel FDPM signals at the incident wavelength is approximately constant at 55 dB while the corresponding fluorescence signal SNR is variable with signal power and is approximately 35 dB. Using these SNR values to guide our studies on the tolerance of absorption and fluorescence-enhanced absorption imaging, we show that the noise tolerance of a Born iterative method far reconstruction of absorption from FDPM measurements at the incident wavelength cannot handle the reduced SNR that is tolerated by a Born iterative type approach for reconstruction of absorption from measurements at the emission wavelength. (C) 2001 Society of Photo-Optical Instrumentation Engineers.
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