Robust video hashing based on representative-dispersive frames

This study proposes a robust video hashing for video copy detection.The proposed method,which is based on representative-dispersive frames(R-D frames),can reveal the global and local information of a video.In this method,a video is represented as a graph with frames as vertices.A similarity measure is proposed to calculate the weights between edges.To select R-D frames,the adjacency matrix of the generated graph is constructed,and the adjacency number of each vertex is calculated,and then some vertices that represent the R-D frames of the video are selected.To reveal the temporal and spatial information of the video,all R-D frames are scanned to constitute an image called video tomography image,the fourth-order cumulant of which is calculated to generate a hash sequence that can inherently describe the corresponding video.Experimental results show that the proposed video hashing is resistant to geometric attacks on frames and channel impairments on transmission.     

电阻层析成像系统敏感场特性分析及图像重建

电阻层析成像系统敏感场受多相流介质分布的影响,敏感场分布数据作为图像重建所需的先验数据必须通过理论计算的方法得到.为了提高重建图像质量,对敏感场分布进行分析是非常必要的.在分析电阻层析成像的基本原理的基础上,采用有限元的方法建立了敏感场的数学模型,通过对敏感场场域的分析,研究了影响敏感场分布的因素及规律,完成了敏感场分布计算及可视化仿真.依据敏感场的计算结果,提出了一种基于多项式加速的ERT图像重建算法,给出了算法的数学模型,并利用谱分析对该算法的收敛性进行了证明.仿真实验结果表明,敏感场有限元模型是正确的,图像重建算法兼备成像质量高及收敛速度快等优点,为ERT图像重建算法的研究提供了一个新方法.     

Efficient parallel implementation of iterative reconstruction algorithms for electron tomography

Electron tomography (ET) combines electron microscopy and the principles of tomographic imaging in order to reconstruct the three-dimensional structure of complex biological specimens at molecular resolution. Weighted back-projection (WBP) has long been the method of choice since the reconstructions are very fast. It is well known that iterative methods produce better images, but at a very costly time penalty. In this work, it is shown that efficient parallel implementations of iterative methods, based primarily on data decomposition, can speed up such methods to an extent that they become viable alternatives to WBP. Precomputation of the coefficient matrix has also turned out to be important to substantially improve the performance regardless of the number of processors used. Matrix precomputation has made it possible to speed up the block-iterative component averaging (BICAV) algorithm, which has been studied before in the context of computerized tomography (CT) and ET, by a factor of more than 3.7. Component-averaged row projections (CARP) is a recently introduced block-parallel algorithm, which was shown to be a robust method for solving sparse systems arising from partial differential equations. It is shown that this algorithm is also suitable for single-axis ET, and is advantageous over BICAV both in terms of runtime and image quality. The experiments were carried out on several datasets of ET of various sizes, using the blob model for representing the reconstructed object.     

Design and modelling of ultrasonic tomography for two-component high-acoustic impedance mixture

This paper describes the design and modelling of ultrasonic tomography for two-component high-acoustic impedance mixture such as liquid/gas and oil/gas flow which commonly found in chemical columns and industrial pipelines. The information obtained through this research has proven to be useful for further development of ultrasonic tomography. This includes acquiring and processing ultrasonic signals from the transducers to obtain the information of the spatial distributions of liquid and gas in an experimental column. Analysis on the transducers’ signals has been carried out to distinguish between the observation time and the Lamb waves. The information obtained from the observation time is useful for further development of the image reconstruction.     

用于两相流电容层析成像系统中的图像重建算法

图像重建算法研究和增加投影数据是改善图像重建质量的两个重要方面。由于目前在ECT系统中存在着一种基于奇异值分解（SVD）的图像重建算法,此算法中的奇异值将对应图像重建矩阵中很大的对角线元素,从而导致伪逆很不稳定。因而讨论了改进的基于奇异值分解（MSVD）的图像重建算法,该算法是用改进奇异值分解方法求出图像重建矩阵。仿真及实验结果均表明该算法是一种实时的、重建图像质量优于SVD。     

基于截面含气率的气／液两相流相关速度测量

基于双截面电阻层析成像技术进行气／液两相流的相关速度测试研究,分析了相关速度测量原理,提出了基于截面含气率的相关测速方法。通过插值增加上游、下游双截面含气率的采样数据,利用独立开发的软件系统具有的数据存储与回放功能对基于截面含气率的相关测速方法进行评定实验。实验结果表明：该方法测速精度较高,且稳定可靠。     

基于神经网络和D-S证据理论辨识两相流流型

提出一种基于电容层析成像(ECT)系统、神经网络和证据理论辨识两相流流型的方法。这种方法采用神经网络与D-S证据理论相结合的方法来辨识两相流流型,并对两相流的几种常见流型进行了辨识。仿真实验结果表明:此种方法在两相流流型辨识中具有较高的判别精度,为两相流流型辨识提供了一种有效的手段。     

A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

基于消光断层法的喷雾分布不均匀度测量技术

燃油喷嘴喷雾分布不均匀度作为衡量喷嘴喷雾性能的重要参数之一,直接影响发动机的燃烧效率以及污染排放。为了验证消光断层法激光式分布器(Statistical Extinction Tomography Scan Optical Patternator,SETscan)在喷雾分布不均匀度方面的测量能力,利用离心式喷嘴在不同燃油工况条件下开展燃油喷嘴雾化性能实验,并与传统机械式测量方法进行了对比。结果表明,相比于传统机械式测量方法,激光式测量方法稳定性更好、实验效率更高、实验数据更加直观,具有更高的时间和空间分辨率,对激光遮挡率为10%~90%的燃油喷雾能够得出合理有效的浓度分布实验结果,同时由于激光式测量方法属于非接触测量,不会受到喷雾本身特性和安装位置等因素的影响。