Application of marching cubes algorithm in visualization of mineral deposits

An implementation scheme of the marching cubes (MC) algorithm was presented for the visualization of mineral deposits. The basic principles, processes and pitfalls of the MC algorithm were discussed. The asymptotic decider algorithm was employed to solve the ambiguity problem associated with the MC algorithm. The implementation scheme was applied to model and reconstruct the surfaces of mineral deposits, using the geological data obtained from an iron mine in China. Experimental results demonstrate the ability of the implementation scheme to solve the ambiguity problem, and illustrate the effectiveness and efficiency of the MC algorithm in the visualization of mineral deposits.

Phantom-based evaluations of two binning algorithms for four-dimensional CT reconstruction in lung cancer radiation therapy

Objective: The purpose of this study was to evaluate the performance of the phase-binning algorithm and amplitude-binning algorithm for four-dimensional computed tomography(4DCT) reconstruction in lung cancer radiation therapy. Methods: Quasar phantom data were used for evaluation. A phantom of known geometry was mounted on a four-dimensional(4D) motion platform programmed with twelve respiratory waves(twelve lung patients trajectories) and scanned with a Philips Brilliance Big bore 16-slice CT simulator. The 4DCT images were reconstructed using both phase- and amplitude-binning algorithms. Internal target volumes(ITVs) of the phase- and amplitude-binned image sets were compared by evaluation of shape and volume distortions. Results: The phantom experiments illustrated that, as expected, maximum inhalation occurred at the 0% amplitude and maximum exhalation occurred at the 50% amplitude of the amplitude-binned 4DCT image sets. The amplitude-binned algorithm rendered smaller ITV than the phase-binning algorithm. Conclusion: The amplitude-binning algorithm for 4DCT reconstruction may have a potential advantage in reducing the margin and protecting normal lung tissue from unnecessary irradiation.

Fast Multilevel CVT-Based Adaptive Data Visualization Algorithm

Efficient data visualization techniques are critical for many scientific applications. Centroidal Voronoi tessellation(CVT) based algorithms offer a convenient vehicle for performing image analysis,segmentation and compression while allowing to optimize retained image quality with respect to a given metric.In experimental science with data counts following Poisson distributions,several CVT-based data tessellation algorithms have been recently developed.Although they surpass their predecessors in robustness and quality of reconstructed data,time consumption remains to be an issue due to heavy utilization of the slowly converging Lloyd iteration.This paper discusses one possible approach to accelerating data visualization algorithms.It relies on a multidimensional generalization of the optimization based multilevel algorithm for the numerical computation of the CVTs introduced in[1],where a rigorous proof of its uniform convergence has been presented in 1-dimensional setting.The multidimensional implementation employs barycentric coordinate based interpolation and maximal independent set coarsening procedures.It is shown that when coupled with bin accretion algorithm accounting for the discrete nature of the data,the algorithm outperforms Lloyd-based schemes and preserves uniform convergence with respect to the problem size.Although numerical demonstrations provided are limited to spectroscopy data analysis,the method has a context-independent setup and can potentially deliver significant speedup to other scientific and engineering applications.

Four-dimensional flow magnetic resonance imaging incirrhosis

Since its introduction in the 1970’s,magnetic resonance imaging(MRI)has become a standard imaging modality.With its broad and standardized application,it is firmly established in the clinical routine and an essential element in cardiovascular and abdominal imaging.In addition to sonography and computer tomography,MRI is a valuable tool for diagnosing cardiovascular and abdominal diseases,for determining disease severity,and for assessing therapeutic success.MRI techniques have improved over the last few decades,revealing not just morphologic information,but functional information about perfusion,diffusion and hemodynamics as well.Four-dimensional(4D)flow MRI,a time-resolved phase contrast-MRI with three-dimensional(3D)anatomic coverage and velocity encoding along all three flow directions has been used to comprehensively assess complex cardiovascular hemodynamics in multiple regions of the body.The technique enables visualization of 3D blood flow patterns and retrospective quantification of blood flow parameters in a region of interest.Over the last few years,4D flow MRI has been increasingly performed in the abdominal region.By applying different acceleration techniques,taking 4D flow MRI measurements has dropped to a reasonable scanning time of 8 to 12 min.These new developments have encouraged a growing number of patient studies in the literature validating the technique’s potential for enhanced evaluation of blood flow parameters within the liver’s complex vascular system.The purpose of this review article is to broaden our understanding of 4D flow MRI for the assessment of liver hemodynamics by providing insights into acquisition,data analysis,visualization and quantification.Furthermore,in this article we highlight its development,focussing on the clinical application of the technique.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Laguerre Simulation and Visualization for Microstructure of Metal Materials

Simulation method was designed to divide Laguerre diagram for right circle group with different weight; out-of-core incremental algorithm for Laguerre diagram was constructed; simulation program development and visualization was done and simulation was realized in user-specified arbitrary area for simulation of metal materials microstructure, which facilitated the practical application and secondary development of Laguerre diagram in the field of material science engineering. Finally, the utilization of a developed software package exemplified the simulation application of microstructure about metal materials and proved its validity.

An improved self-calibration approach based on adaptive genetic algorithm for position-based visual servo

An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi＇s self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi＇s approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.

Dynamic Visualization Approach of the Multiphase Flow Using Electrical Capacitance Tomography

Identifying the flow patterns is vital for understanding the complicated physical mechanisms in multiphase flows.For this purpose,electrical capacitance tomography(ECT) technique is considered as a promising visualization method for the flow pattern identification,in which image reconstruction algorithms play an important role.In this paper,a generalized dynamic reconstruction model,which integrates ECT measurement information and physical evolution information of the objects of interest,was presented.A generalized objective functional that simultaneously considers the spatial constraints,temporal constraints and dynamic evolution information of the objects of interest was proposed.Numerical simulations and experiments were implemented to evaluate the feasibility and efficiency of the proposed algorithm.For the cases considered in this paper,the proposed algorithm can well reconstruct the flow patterns,and the quality of the reconstructed images is improved,which indicates that the proposed algorithm is competent to reconstruct the flow patterns in the visualization of multiphase flows.

Neural saliency algorithm guide bi-directional visual perception style transfer

The artistic style transfer of images aims to synthesise novel images by combining the content of one image with the style of another,which is a long-standing research topic and already has been widely applied in real world.However,defining the aesthetic perception from the human visual system is a challenging problem.In this study,the authors propose a novel method for automatic visual perception style transfer.First,they render a novel saliency detection algorithm to automatically perceive the visual attention of an image.Then,different from conventional style transfer algorithm in which style transferring is applied uniformly across all image regions,the authors apply a saliency algorithm to guide the style transferring process,enabling different types of style transferring to occur in different regions.Extensive experiments show that the proposed saliency detection algorithm and the style transfer algorithm are superior in performance and efficiency。

Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea

The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).Alternatively,physical space analysis system(4D-PSAS)is proposed to reduce the computation cost,in which the 4D-Var problem is solved in physical space(i.e.,observation space).In this study,the conjugate gradient(CG)algorithm,implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process.The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed.In order to overcome the non-monotonic variation of gradient norm,a new algorithm,Minimum Residual(MINRES)algorithm,is implemented in the process of assimilation iteration in this study.Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function,greatly improves the convergence properties of 4D-PSAS as well,and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.

Visualization and Damage Assessment for Flooded Area

A practical method for visualizing flood area and evaluating damage is presented, which consists of two technical approaches: self\|programming and adapting commercial GIS platforms. The low\|cost and easy\|to\|use GIS\|Based model developed by self\|programming can meet current requirements of most local authorities, especially in developing countries. In this model, two cases, non\|source flood and source flood, are distinguished and the Seed\|spread algorithm suitable for source\|flood is discussed; The flood damage is assessed by overlaying the flood area range with thematic maps and other related social and economic data. and all thematic maps are converted to raster format before overlay analysis. Two measures are taken to improve the operation efficiency of speed seed\|spread algorithm. The accuracy of the model mainly depends on the resolution and precision of the DEM data, and the accuracy of registering all raster layers and the quality of attribute data.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

基于HTML的Web系统在植保机中的应用研究

为了实现农田信息的实时监控,将农田信息进行可视化处理,设计了本系统。植保机作为农田中数据采集节点和地面工作站的中继站,可实现将农田信息上传地面工作站,且可利用HTML完成农田数据的Web可视化处理。同时,依据通讯路径损耗,确定通讯频率为430M,植保机飞行高度为750 m;基于模拟退火算法,实现植保机飞行路径规划;利用HTML完成用户Web网页设计;并对系统进行测试。测试结果表明:土壤湿度监控精度相对误差分布区间为[0.57%, 2.79%],Web网页可以实现各数据节点农田信息的实时显示。

PDF417二维条码的编码技术及其在Visual Basic下的实现

应用VisualBasic对PDF4 17条码进行了编码 .针对编码模块的不同算法 ,提出了关于编码溢出、汉字编码等问题的讨论及解决方法 .从而实现了PDF4 17编码程序在信息录入中的应用 。

基于visual c#的自适应路由算法仿真实验设计

针对计算机通信网实验教学存在的部分缺陷,采用Visual C#编程工具开发了自适应距离路由选择算法仿真实验系统。实践表明,该实验系统通过窗体和控件的合理选择与配置,动态地演示单个路由器和整个网络基于距离矢量路由算法的路由过程及结果,为学生方便、直观地理解和掌握距离矢量路由算法的工作原理提供有效帮助。

基于OpenCV下的Visual C++数字图像处理方法

OpenCV是一个开放源代码的机器视觉库,在应用Visual C++进行程序开发的过程中,可以直接调用OpenCV视觉库中的特定函数,开发出自己的图像处理程序。通过示例程序"人脸识别",阐述将Visual C++与OpenCV相结合进行数字图像处理程序开发的过程。

基于Visual C++云台控制系统关键技术与算法

介绍了用VC++程序设计实现云台的计算机控制的关键技术和算法,关键技术是通过串口/并口按照解码器约定的数据协议准确地发送指令数据.并对使用WinIO库提供的接口函数实现数据通信的方法和WinIO库的使用步骤作了说明.

基于YOLOv5的智能化精准物料分离系统

针对现代农业生产过程中物料分离和分类的需求,设计一款基于YOLOv5目标检测模型的可视化智能微型物料分离机。该设计利用基于ARM内核的STM32微控制器为核心,采用筛网与舵机结合的复合式设计,承担待分离物料的初筛工作;结合YOLOv5算法对物料进行更精细化的图像识别,提高分离的精确度;结合蓝牙模块实现非接触式远距离传输和控制数据。实验证明,该系统不仅能够实时准确地分离不同类型的物料,同时采用可视化界面使操作更加便捷,为提高物料分离的质量和效率提供了高效可行的解决方案。

基于Visual Foxpro的EIGamal数字签名算法

数字签名的使用越来越广泛,其主要的实现方法是公钥密码算法.ElGamal算法是一种比较常用的公钥密码算法,既可以用于加密又可以用于签名,具有生成速度快、签名验证简单的特点.VFP(Visual Foxpro)是一种使用比较广泛的面向对象的数据库设计系统,基于VFP的签名实现方法比较少.介绍ElGamal数字签名算法在VFP中的实现,阐述了ElGamal数字签名算法的原理,详细介绍了算法的实现过程,最后给出在VFP中ElGamal数字签名的实现界面.