Single-Phase Velocity Determination Based in Video and Sub-Images Processing:An Optical Flow Method Implemented with Support of a Programmed MatLab Structured Script

Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.

An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect videos.However,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant information.Hence,to automate this manual procedure of video assessment,an accurate and efficient automated system is a greater necessity.From this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy(PME)method tofirst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to thefield of underwater archaeology,stock assessment,and surveillance.

Realtime Object Detection Through M-ResNet in Video Surveillance System

Object detection plays a vital role in the video surveillance systems.To enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and banks.However,monitor-ing the video continually at a quicker pace is a challenging job.As a consequence,security cameras are useless and need human monitoring.The primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful actions.The anomalous action does not occur at a high-er rate than usual occurrences.To detect the object in a video,first we analyze the images pixel by pixel.In digital image processing,segmentation is the process of segregating the individual image parts into pixels.The performance of segmenta-tion is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance sys-tem.In this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient light.Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream.The proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.

Projectile-Borne Video Reconnaissance System

Aiming at applications as a projectile-borne video reconnaissance system, the overall design and prototype in principle of a mortar video reconnaissance system bomb were developed. Mortar launched test results show that the initial integrated system was capable of transmitting images through tens of kilometers with the image resolution identifying effectively tactical targets such as roads, hills, caverns, trees and rivers. The projectile-borne video reconnaissance system is able to meet the needs of tactical target identification and battle damage assessment for tactical operations. The study will provide significant technological support for further independent development.

基于USB Video Camera的自动对焦系统

介绍一种应用于USB video camera中的自动对焦系统。由USB video camera获取的视频图像经计算机进行FFT运算或微分运算,得到其频谱幅值数据或微分幅值数据,计算机根据所得数据判断USB video camera中的镜头是否处于离焦位置并控制电机将镜头移到对焦位置。文章还进一步讨论了提高自动对焦准确度的措施。实验结果表明该自动对焦系统能很好地实现USB video camera的自动对焦,该系统将使具有USB接口的video camera使用更简单方便。

Video Based Vehicle Detection and its Application in Intelligent Transportation Systems

Video based vehicle detection technology is an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and comprehensive vehicle behavior data collection capabilities. This paper proposes an efficient video based vehicle detection system based on Harris-Stephen corner detector algorithm. The algorithm was used to develop a stand alone vehicle detection and tracking system that determines vehicle counts and speeds at arterial roadways and freeways. The proposed video based vehicle detection system was developed to eliminate the need of complex calibration, robustness to contrasts variations, and better performance with low resolutions videos. The algorithm performance for accuracy in vehicle counts and speed was evaluated. The performance of the proposed system is equivalent or better compared to a commercial vehicle detection system. Using the developed vehicle detection and tracking system an advance warning intelligent transportation system was designed and implemented to alert commuters in advance of speed reductions and congestions at work zones and special events. The effectiveness of the advance warning system was evaluated and the impact discussed.

Temporal Shape Error Concealment for Video Objects

A novel temporal shape error concealment technique is proposed, which can he used in the context of object-based video coding schemes. In order to reduce the effect of the shape variations of a video object, the curvature scale space （CSS） technique is adopted to extract features, and then these features are used for boundary matching between the current frame and the previous frame. Because the temporal, spatial and sta- tistical video contour information are all considered, the proposed method can find the optimal matching, which is used to replace the damaged contours. The simulation results show that the proposed algorithm achieves better subjective, objective qualities and higher efficiency than those previously developed methods.

Automated neurosurgical video segmentation and retrieval system

Medical video repositories play important roles for many health-related issues such as medical imaging, medical research and education, medical diagnostics and training of medical professionals. Due to the increasing availability of the digital video data, indexing, annotating and the retrieval of the information are crucial. Since performing these processes are both computationally expensive and time consuming, automated systems are needed. In this paper, we present a medical video segmentation and retrieval research initiative. We describe the key components of the system including video segmentation engine, image retrieval engine and image quality assessment module. The aim of this research is to provide an online tool for indexing, browsing and retrieving the neurosurgical videotapes. This tool will allow people to retrieve the specific information in a long video tape they are interested in instead of looking through the entire content.

Research on video motion object segmentation for content-based application

With the development of the modern information society, more and more multimedia information is available. So the technology of multimedia processing is becoming the important task for the irrelevant area of scientist. Among of the multimedia, the visual informarion is more attractive due to its direct, vivid characteristic, but at the same rime the huge amount of video data causes many challenges if the video storage, processing and transmission.

VideoLog可视化测井油管接箍自动识别方法

在可视化测井中,深度对于判断油管缺陷位置至关重要,而现有的测深系统具有一定的深度误差。实际工程中,可通过识别油管接箍再参照油管数据表来准确标定仪器的深度。本文基于运动视频图像处理,提出了一种油管接箍自动识别方法。利用VideoLog可视化测井系统采集井下油管视频图像,通过对视频图像进行形态学处理、特征参数提取、接箍判决等过程来准确识别接箍。实验结果表明,同一个接箍在视频中会多次出现,也会被多次识别到,同一接箍平均识别率为86.9%,接箍计数的正确率为100%。方法已成功用于可视化测井视频解释处理中,取得了较好的工程应用效果。

Hardware Implementation and System Control Strategy of Videoconference System Based on Rec. H. 261

The design and realization of a videoconference system based on international recommendation are introduced in this paper, and the hardware implementation of video codec based on ITU-T H. 261 is briefly discussed. Furthermore, the buffer control method and the adaptive control strategy for quantization are proposed, which are adaptive and robust. This system can be operated under the transmission rate ranging from 128kb/s to 2Mb/s. With these strategies for the videoconference system, the high quality image is obtained. The time delay of the system is about half a second.

Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR:A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks

This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated.

Detecting Objectionable Videos

This paper addresses the problem of detecting objectionable videos, which has never been carefully studied before. Our method can be efficiently used to filter objectionable videos on Internet. One tensor based key-frame selection algorithm, one cube based color model and one objectionable video estimation algorithm are presented. The key frame selection is based on motion analysis using the three-dimensional structure tensor. Then the cube based color model is employed to detect skin color in each key frame. Finally, the video estimation algorithm is applied to estimate objectionable degree in videos. Experimental results on a variety of real-world videos downloaded from Internet show that this method is promising.

智能行车记录仪图像去雾系统的FPGA设计

雾霾天气下,交通道路能见度低,导致所采集到的视频画面退化、图像信息模糊,同时考虑传统系统处理实时性不高等问题,基于ZYNQ平台设计了一种图像去雾系统,并应用于智能行车记录仪系统中.首先,针对传统暗通道去雾算法在天空区域存在失真等问题,提出了一种分割天空区域的策略来修正图像复原参数;然后,针对计算全局大气光值时,需对整幅图像的像素排序消耗大量资源的问题,利用现场可编程门阵列(FPGA)并行运算的优势,提出一种帧迭代方法优化求取大气光值,同时优化了引导滤波的硬件设计;最后,将双路高清多媒体接口(HDMI)资源中,一路作为视频输入,另一路作为视频处理输出,搭建实时交通图像视频处理试验平台.试验结果表明,系统针对雾霾天气下的交通视频具有较好的去雾效果,尤其是可以解决天空区域去雾的失真问题.在对分辨率为1280像素×720像素的交通视频去雾时,可以达到30帧/s的处理速度,满足实时性要求.

视频侦查技术关键及其发展展望

视频侦查技术的核心是通过视频图像的提取、查看、分析和研判来获取侦查线索、固定涉案证据。多年来经过在实战应用中不断地发展创新,视频侦查技术形成了自己独特的关键技术体系:视频信息分析解读技术是侦查应用和证据固定的基础和核心;视频证据固定保全技术的规范是审判中心主义的客观要求,可以获取视频侦查记录报告、视频检验鉴定报告或视频数据关联报告;低质量视频图像的增强恢复技术专业性强,应用范围窄,技术成熟度高,然而它不断面临新的挑战。目前,视频数据的智能应用在大数据背景下变得越来越重要,仍需进一步突破视频自动识别技术的应用范畴,建立完善多层次的视频数据综合应用体系,打造适应不同业务需要的视频数据实战应用模型。

基于可能性理论的红外与可见光视频自适应分层融合算法

针对目前红外与可见光视频融合模型无法根据视频间差异特征动态调整融合策略,造成融合效果差甚至失效等问题,提出了一种基于可能性理论的红外与可见光视频的自适应分层融合算法。计算视频序列每帧中感兴趣区域的各类差异特征的幅值大小,得到每帧对应的主要差异特征;搭建分层融合框架,并确定各层变元,基于余弦相似性计算不同变元对各差异特征的融合有效度,利用可能性理论构造相应的融合有效度分布;逐层分析不同变元对各类差异特征的融合效果,选择出每层的最优变元;通过变元间的优化组合实现红外与可见光视频的自适应分层融合。实验结果表明:所提算法在保留红外典型目标和可见光结构细节等方面取得了显著的融合效果,并且在定量分析和定性评价上明显优于单一融合算法。

基于AMP架构的青霉素结晶与发酵检测系统设计

目前,在青霉素工业生产中,人工检测是青霉素结晶状态以及发酵液高度检测的主要方式,这种方式成本高、自动化水平低且易造成误判,对青霉素产量和品质有很大影响。针对此种情况,文中设计了一种基于AMP架构的青霉素结晶状态与发酵液高度检测系统。该系统以ZYNQ-7000作为可重构嵌入式平台。在PL部分,利用Vivado HLS工具实现结晶状态、发酵液高度检测的图像预处理算法IP核设计,并搭建出视觉传感系统的硬件平台。在PS部分,系统采用双核AMP(非对称多处理)运行模式,完成结晶状态检测、发酵液高度检测算法处理与以太网视频传输、图像存储功能的并行处理。实验结果表明:该系统支持2种检测模式的切换,可实现对工业生产中青霉素结晶状态与发酵液高度的精确检测与视频数据的实时传输。

涉水视觉

地球表面有约71%的面积被江河湖海等水体覆盖,陆地上的成像也会受到云雪雨雾等水体影响,但是,当前常见的机器视觉科研工作和应用系统基本只围绕空气和真空介质中的视觉任务展开,涉及不同形态水体的视觉工作没有得到系统的研究.涉水视觉(water-related vision)作为涉水光学技术在视觉领域的具象化体现,重点研究光与水的物质相互作用及跨介质传播过程中,涉水视觉影像信号智能处理与分析方面的科学问题,以及先进智能涉水视觉装备研制方面的工程技术问题.本文从“为什么大海是蓝色的?”这一具有普适意义的问题出发,系统介绍了水对光的吸收、散射、衰减作用机理,对涉水视觉任务造成的影响,以及现有的涉水图像处理与解析方法.本文基于水体光学特性及成像退化机理,介绍了团队在探索涉水成像和图像解析等涉水视觉关键技术及装备方面的成果,先后研制了全海深超高清相机“海瞳”、全海深3D相机、全海深高清摄像机等,形成了从色彩、强度、偏振、光谱等全方位、体系化的水下观测解析装备研制能力,填补了我国全海深光学视觉技术的空白,推动了我国涉水视觉领域技术的升级,应用价值和社会效益显著.

基于深度学习的监控视频图像增强处理方法

传统的图像增强处理方法常存在图像清晰度较低、失真度较高的问题。因此,提出基于深度学习的增强处理方法。在获取监控视频图像信息后,对图像进行序列对齐处理。通过图像量化处理的方式显示单个图像中的关键像素点和邻近像素点之间的分布。采用深度学习技术,用8个方向的梯度算子提取反射图像的方向梯度,得到细节增强的反射图像,再通过Retinex反变换完成增强处理。设计对比实验,证明该方法可以改善监视视频的图像增强效果,获得更清楚的景物图片。

新工科背景下数字图像与视频处理课程混合式教学的探索与实践

新工科发展背景下数字图像与视频处理课程传统单一的教学模式和教学方法已无法有效地适应新时代高素质人才培养要求,新工科要求课程的教学内容、教学模式和考核方式等要素不断更新。为改进陈旧的教学模式、激发学生的学习主动性、提升学生的创新思维及编程实践能力,提出通过开展线上线下混合式教学、线下课堂实施六步教学法、课程资源建设、融合课程思政、构建验证性基础实验+综合实验+创新性项目实践的阶梯型实践教学体系、改进教学评价方式等多种改进措施,深化课程教学改革,提高了课程教学质量,取得了良好的教学效果。