Real-time image processing and display in object size detection based on VC++

Real-time detection for object size has now become a hot topic in the testing field and image processing is the core algorithm. This paper focuses on the processing and display of the collected dynamic images to achieve a real-time image pro- cessing for the moving objects. Firstly, the median filtering, gain calibration, image segmentation, image binarization, cor- ner detection and edge fitting are employed to process the images of the moving objects to make the image close to the real object. Then, the processed images are simultaneously displayed on a real-time basis to make it easier to analyze, understand and identify them, and thus it reduces the computation complexity. Finally, human-computer interaction （HCI）-friendly in- terface based on VC ＋＋ is designed to accomplish the digital logic transform, image processing and real-time display of the objects. The experiment shows that the proposed algorithm and software design have better real-time performance and accu- racy which can meet the industrial needs.

A Flexible DSP-Based Network forReal-Time Image-Processing

This paper proposed a general purpose real-time image processing system based on a flexible DSP-based Network, which is implemented by a high bandwidth communication channel, links. The links is realized using FPGA and provides a bandwidth of 12. 8 Gbit/s. Using the links, The topologic of multi-DSP system can be changed online to meet the variabilities of the parallel algorithm of image processing. The system can be assembled with utmost tens of boards and maintain the high communication speed. Analysis of the system adaptivity to image processing is testified followed by actual results. Key words real-time image processing - multi-DSP - flexible - scalable - FPGA - links CLC number TP 303 Foundation item: Supported by the National Natural Science Foundation of China (60135020)Biography: MAO Hai-cen(1973-), male, Ph.D. candidate, research direction: artificial intelligence, expert system, pattern recognition and image processing

New multi-DSP parallel computing architecture for real-time image processing

The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is presented. It has many promising characteristics such as powerful computing capability, broad I/O bandwidth, topology flexibility, and expansibility. The parallel system performance is evaluated by practical experiment.

High performance reconfigurable hardware system for real-time image processing

A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-BSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports ＆ serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems （RTOS） by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance convenient. Thus it is suitable for real-time image processing and can get satisfactory effect of image recognition.

An FPGA-based real-time image processing system

This paper analyzes the current difficulties encountered in on-line inspection systems of strip surface quality, specifically relating to problems with real-time processing of huge amounts of data. To address this need, this paper describes an FPGA-based high-speed image processing module with both hardware and software aspects. Improving these two aspects together will help the system achieve real-time processing of massive image data, and simplifies the architecture of the strip surface quality on-line inspection system.

A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

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.

Application of TMS320C80 in Image Processing

To study the application of TMS320C80 in image processing, an image processing system was designed based on this device, and the task of real time image processing was well accomplished on the hardware platform. TMS320C80 architecture's high degree of on chip integration and software flexibility will make it widely used in image processing that requires high processing speeds.

Heterogeneity induced strain localization in block-in-matrix-soils subjected to uniaxial loading using real-time CT scanning

Block-in-matrix-soils(bimsoils)are geological mixtures that have distinct structures consisting of relatively strong rock blocks and weak matrix soils.It is still a challenge to evaluate the mechanical behaviors of bimsoils because of the heterogeneity,chaotic structure,and lithological variability.As a result,only very limited laboratory studies have been reported on the evolution of their internal deformation.In this study,the deformation evolution of bimsoils under uniaxial loading is investigated using real-time X-ray computed tomography(CT)and image correlation algorithm(with a rock block percentage(RBP)of 40%).Three parameters,i.e.heterogeneity coefficient(K),correlation coefficient(CC),and standard deviation(STD)of displacement fields,are proposed to quantify the heterogeneity of the motion of the rock blocks and the progressive deformation of the bimsoils.Experimental results show that the rock blocks in bimsoils are prone to forming clusters with increasing loading,and the sliding surface goes around only one side of a cluster.Based on the movement of the rock blocks recorded by STD and CC,the progressive deformation of the bimsoils is quantitatively divided into three stages:initialization of the rotation of rock blocks,formation of rock block clusters,and formation of a shear band by rock blocks with significant rotation.Moreover,the experimental results demonstrate that the meso-motion of rock blocks controls the macroscopic mechanical properties of the samples.

Image processing algorithm acceleration using reconfigurable macro processor model

The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented. Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of 'hardware' function that can be called by the DSP in high-level algorithm. It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

A robust system for real-time pedestrian detection and tracking

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.

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.

Simulation and Implementation of Real-Time Vision-Based Control System for 2-DoF Robotic Arm Using PID with Hardware-in-the-Loop

Microsoft Kinect sensor has shown the research community that it's more than just an interactive gaming device, due to its multi-functional abilities and high reliability. In this work, online HIL (Hardware-in-the-Loop) experimental data are used to apply human motion imitation to a 2-degree of freedom Lego Mind storm NXT robotic arm. A model simulation of the dc motor used in this experiment is also present in this paper. The acquired input data from the Kinect sensor are processed in a closed loop PID controller with feedback from motors encoders. The applied algorithms solve the overlapping input problem, conducting a simultaneous control of both shoulder and elbow joints, and solving the overlapping input problem as well. The work in this paper is presented as a prototype to assure the applicability of the algorithms, for further development.

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.

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.