An Example of Machine Vision Applied in Printing Quality Checking——Research on the Checking of Printing Quality by Image Processing

The traditional printing checking method always uses printing control strips,but the results are not very well in repeatability and stability. In this paper,the checking methods for printing quality basing on image are taken as research objects. On the base of the traditional checking methods of printing quality,combining the method and theory of digital image processing with printing theory in the new domain of image quality checking,it constitute the checking system of printing quality by image processing,and expound the theory design and the model of this system. This is an application of machine vision. It uses the high resolution industrial CCD(Charge Coupled Device) colorful camera. It can display the real-time photographs on the monitor,and input the video signal to the image gathering card,and then the image data transmits through the computer PCI bus to the memory. At the same time,the system carries on processing and data analysis. This method is proved by experiments. The experiments are mainly about the data conversion of image and ink limit show of printing.

Computer Vision Technology for Fault Detection Systems Using Image Processing

In the period of Industries 4.0,cyber-physical systems(CPSs)were a major study area.Such systems frequently occur in manufacturing processes and people’s everyday lives,and they communicate intensely among physical elements and lead to inconsistency.Due to the magnitude and importance of the systems they support,the cyber quantum models must function effectively.In this paper,an image-processing-based anomalous mobility detecting approach is suggested that may be added to systems at any time.The expense of glitches,failures or destroyed products is decreased when anomalous activities are detected and unplanned scenarios are avoided.The presently offered techniques are not well suited to these operations,which necessitate information systems for issue treatment and classification at a degree of complexity that is distinct from technology.To overcome such challenges in industrial cyber-physical systems,the Image Processing aided Computer Vision Technology for Fault Detection System(IM-CVFD)is proposed in this research.The Uncertainty Management technique is introduced in addition to achieving optimum knowledge in terms of latency and effectiveness.A thorough simulation was performed in an appropriate processing facility.The study results suggest that the IM-CVFD has a high performance,low error frequency,low energy consumption,and low delay with a strategy that provides.In comparison to traditional approaches,the IM-CVFD produces a more efficient outcome.

Automated Angle Detection for Industrial Production Lines Using Combined Image Processing Techniques

Angle detection is a crucial aspect of industrial automation,ensuring precise alignment and orientation ofcomponents in manufacturing processes.Despite the widespread application of computer vision in industrialsettings,angle detection remains an underexplored domain,with limited integration into production lines.Thispaper addresses the need for automated angle detection in industrial environments by presenting a methodologythat eliminates training time and higher computation cost on Graphics Processing Unit(GPU)from machinelearning in computer vision(e.g.,Convolutional Neural Networks(CNN)).Our approach leverages advanced imageprocessing techniques and a strategic combination of algorithms,including contour selection,circle regression,polar warp transformation,and outlier detection,to provide an adaptive solution for angle detection.By configuringthe algorithm with a diverse dataset and evaluating its performance across various objects,we demonstrate itsefficacy in achieving reliable results,with an average error of only 0.5 degrees.Notably,this error margin is 3.274times lower than the acceptable threshold.Our study highlights the importance of accurate angle detection inindustrial settings and showcases the reliability of our algorithm in accurately determining angles,thus contributingto improved manufacturing processes.

Study of image processing for V-shape groove and robotic weld seam tracking based on laser vision

Single-stripe laser was applied to acquire V-shape groove contour information. Most of arc light and splash noise was removed and stripe laser image was kept by wavelet transform. Half-threshold algorithm was used for image segmentation and stripe laser image was gotten after refining. Weld seam center position was identified and extracted by extreme curvature corner detection method. The location of torch was detected to accord the frequency of computer program with robot program and serial communication program. The tracking experiments of sidelong, reflex and curve weld line show that the system can meet the demand of the tracking precision under normal welding conditions.

Study on the image processing of laser vision seam tracking system

Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.

Surface Inspection System for Cold Rolled Strips Based on Image ProcessingTechnique

A new surface inspection system for cold rolled strips based on image processing is introduced. The system is equipped withtwo different illumination structures and CCD matrix cameras. The structure and image processing of the inspection system are described. Some efficient algorithms for image processing and classification are presented. The system is tested with strip samples fromcold rolling plants. The results show that the system can detect and recognize six common defects of cold rolled strips successfully.

APPLICATION OF MVP IN REAL TIME IMAGE PROCESSING

MVP is a digital signal processor, which is of MIMD structure and fit for multimedia application. MVP has several processors in it, and its operation is characteristic of parallelism and pipeline; therefore, real-time signal processing can be done on it. This paper presents the image processing system based on MVP, explains the principles of parallel task assignment and hardware pipeline design, and gives out the example of target tracking and edge detection.

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.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Blind Image Quality Assessment Based on Hybrid Fuzzy-Genetic Technique

A new method for no-reference image quality assessment based on hybrid fuzzy-genetic technique is proposed. Noise variance and edge sharpness level of the restored image are two basic metrics for assessing the performance of the restoration algorithm, then a fuzzy if-then inference system is developed to combine the two metrics to get a final quality score, and the parameters of the fuzzy membership function are trained with genetic algorithms. Experiments results show that the image quality score correlates well with mean opinion score and the proposed approach is robust and effective.

Automatic Traffic Using Image Processing

The frequent traffic jams at major intersections call for an effective management system. The paper suggests implementing a smart traffic controller using real-time image processing. The sequence of the camera is analyzed using different edge detection algorithms and object counting methods. Previously they used matching method that means the camera will be installed along with traffic light. It will capture the image sequence. To set an image of an empty road as a reference image, the captured images are sequentially matched using image matching;but in my paper, we used filtering method, which filtered the image and released all waste objects and only showed the cars, and after it well showed the number of cars in image. My paper is software that takes a picture or video. It has been customized to be used in the future to control the traffic light sign by giving each sign sufficient time, depending on the number of cars on each direction.

Sonar Image Processing System for an Autonomous Underwater Vehicle(AUV)

Sonar image processing system is an important intelligent system of Autonomous Un-derwater Vehicle.Based on TMS320C30 high speed DSP,it is used to realize sonar imagecompression and underwater object detections including obstacle recognition in real time.Inthis paper,the software and hardware designs of this system are introduced and the experi-mental results are given.

Feeding Control System of Conveyor-belt Based on Image Processing

Based on the real time measurement of the width of coal flow, the method for measuring the width and the relative position of coal flow on a conveyor-belt by image processing was presented. A feeding control system of conveyor-belt was proposed using a fuzzy controller. This control system consists of CCD camera, universal image sampling system, control network and executor. The result shows that the algorithm used in the image processing is simple and efficient, and the measuring error of width is less than 4%.

No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.

Significance of an Advanced Image-Based Virtual Monoenergetic Reconstruction of Dual Source Dual-Energy CT Data at Low keV Increases Image Quality for Portal Vein System of Pancreatic Cancer Patients

Purpose: To explore the significance of dual-source computed tomography (DECT) virtual monoenergetic reconstructions technology in improving image quality for portal vein system of pancreatic cancer patients. Materials and methods: 47 patients with clinically suspected pancreatic cancer (all confirmed by pathology) were collected. Routine plain scan was performed with Siemens Force dual-source dual-energy CT followed by 3 scans respectively carried out in arterial phase, portal phase and delayed phase. Traditional virtual monoenergetic reconstructions (Mono_E) and new generation of virtual monoenergetic reconstructions (Mono+) were respectively performed on portal vein images to obtain virtual single energy images including Mono_ E70 keV, Mono_E 55 keV and Mono+ 70 keV and Mono+ 55 keV. The signal-to-noise ratio (SNR) and noise of portal vein, normal pancreatic tissues and pancreatic lesions of 100 kV, Mono_E and Mono+ images were compared. In addition, the contrast noise ratio of portal vein and lesions as well as pancreatic tissues and lesions (CNR PV, CNRtumor) were also compared. At the same time, two imaging physicians with rich clinical experiences read the films and scored the images of each group by using the 5-point scoring method. Results: Mono+ 55 keV images including SNRpv, SNRpanc, SNRtumor, Noise, CNRpv, CNRtumor were statistically different from 100 KV images and Mono_E images (P < 0.05). As for the subjective score, Mono+ 55 keV image score also had the highest score, which had statistical significance (P < 0.05). The results showed that Mono+ 55 keV images had the best quality. Conclusion: The new generation of virtual Mono+ post-treatment can reduce image noise. Low energy Mono+ images can improve the contrast between pancreatic cancer lesions and portal of pancreatic cancer patients.

Automated quality inspection of baby corn using image processing and deep learning

The food industry typically relies heavily on manual operations with high proficiency and skills.According to the quality inspection process,a baby corn with black marks or blemishes is considered a defect or unqualified class which should be discarded.Quality inspection and sorting of agricultural products like baby corn are labor-intensive and time-consuming.The main goal of this work is to develop an automated quality inspection framework to differentiate between‘pass’and‘fail’categories based on baby corn images.A traditional image processing method using a threshold principle is compared with relatively more advanced deep learning models.Particularly,Convolutional neural networks,specific sub-types of deep learning models,were implemented.Thorough experiments on choices of network architectures and their hyperparameters were conducted and compared.A Shapley additive explanations(SHAP)framework was further utilized for network interpretation purposes.The EfficientNetB5 networks with relatively larger input sizes yielded up to 99.06%accuracy as the best performance against 95.28%obtained from traditional image processing.Incorporating a region of interest identification,several model experiments,data application on baby corn images,and the SHAP framework are our main contributions.Our proposed quality inspection system to automatically differentiate baby corn images provides a potential pipeline to further support the agricultural production process.

Cucumber appearance quality detection under complex background based on image processing

Cucumber fruit appearance quality is an important basis of growth status.In order to improve the quality detection accuracy and processing efficiency of cucumber color image under complicated background,an improved GrabCut algorithm was proposed to extract the cucumber boundary.Firstly,including pixel size normalization,rectangular box set and scale image resolution,pretreatments of cucumber image were adopted to reduce the iteration times and operation time of GrabCut algorithm.Then,the Gaussian mixture model was chosen to find out the possible prospect of target region and background region in the preprocessed rectangular frame on the preliminary modeling.Meanwhile,by the optimization of K-means cluster to the initial GMM model,the effective target area was extracted.Finally,the whole image noise and serrated boundary was removed by morphological operations to segment the outline of the complete target prospects with appropriate structure size.And then the cucumber appearance quality detection instrument was designed to extract the texture and shape features exactly,so that it could obtain cucumber appearance quality and evaluate its growth effectively.With the segmentation experiments by almost 300 cucumber original images from greenhouse in Shandong Province,the results showed that the improved GrabCut algorithm could effectively extract the complete and smooth boundary of cucumber.With relatively high segmentation evaluation index,the precision was 93.88%,the recall rate was 99.35%,the F-Measure reached 96.53%,and the misclassification error was controlled at minimum 5.84%.The average running time was shortened to 1.4023 s.The comparison results showed that the improved GrabCut algorithm was the best,followed by the SLIC and traditional GrabCut method.Cucumber appearance quality detection instrument could also extract more accurate feature parameters.And it could meet the basic growth condition assessment by automatic image processing.

Control and image processing for streak tube imaging lidar based on VB and MATLAB

In this letter, we develope a control and image processing system for Streak Tube Imaging Lidar （STIL）. In the system, the data acquisition card control and the software interface are programmed in Visual Basic （VB） while the image processing is finished by MATLAB. A STIL imaging experiment is carried out in the laboratory. We obtained the intensity and range images of targets with pseudo color by image processing and reconstruction for a set of raw streak images of targets at different distances acquired by STIL. The range resolution is better than 2 centimeters.