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...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.展开更多
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...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.展开更多
An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Point...An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.展开更多
Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturin...Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.展开更多
Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques...Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.展开更多
Fluorescence microscopy has become an essential tool for biologists,to visualize the dynamics of intracellular structures with specific labeling.Quantitatively measuring the dynamics of moving objects inside the cell ...Fluorescence microscopy has become an essential tool for biologists,to visualize the dynamics of intracellular structures with specific labeling.Quantitatively measuring the dynamics of moving objects inside the cell is pivotal for understanding of the underlying regulatory mechanism.Protein-containing vesicles are involved in various biological processes such as material transportation,organelle interaction,and hormonal regulation,whose dynamic characteristics are signi¯cant to disease diagnosis and drug screening.Although some algorithms have been developed for vesicle tracking,most of them have limited performance when dealing with images with low resolution,poor signal-to-noise ratio(SNR)and complicated motion.Here,we proposed a novel deep learning-based method for intracellular vesicle tracking.We trained the U-Net for vesicle localization and motion classification,with demonstrates great performance in both simulated datasets and real biological samples.By combination with fan-shaped tracker(FsT)we have previously developed,this hybrid new algorithm significantly improved the performance of particle tracking with the function of subsequently automated vesicle motion classification.Furthermore,its performance was further demonstrated in analyzing with vesicle dynamics in different temperature,which achieved reasonable outcomes.Thus,we anticipate that this novel method would have vast applications in analyzing the vesicle dynamics in living cells.展开更多
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 achie...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.展开更多
A new image processing method based on the high-speed camera is proposed to identify,locate,and track clusters.The instantaneous characteristic parameters of particle clusters in the riser of the circu-lating fluidize...A new image processing method based on the high-speed camera is proposed to identify,locate,and track clusters.The instantaneous characteristic parameters of particle clusters in the riser of the circu-lating fluidized bed(CFB)can be acquired,such as solids holdup,vertical velocity,lateral displacement,aspect ratio and near-circularity.Experiments were carried out with glass bead particles,river sand particles and FCC particles.The time series of images of gas-solid flow in a CFB riser with a 100 mm x 25 mm cross-section and 3.2 m in length were obtained using high-speed cameras.The k-means++clustering algorithm is utilized to identify the clusters,centroid is applied to locate the clusters,and the cross-correlation algorithm is employed to track the specific clusters and number them to get the instantaneous characteristic parameters.The results illustrate that the shapes of clusters in the center area are closest to circle,moving upwards at a uniform speed,while the clusters in the side-wall area are mostly elongated or long chain-like,moving slowly downwards.In the transition area,the clusters are more complex,moving upwards at a constant speed,and having large lateral displacement.The results show that the image processing method used in this study is successful in acquiring the dynamic and structural parameters of the clusters simultaneously.展开更多
A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective....A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.展开更多
A new efftcient straight line detection algorithm, GPI （ Gray Projecting Integral） method is proposed. The gray values of a sub-window are projected onto a line, and sum the gray values which are projected onto one ...A new efftcient straight line detection algorithm, GPI （ Gray Projecting Integral） method is proposed. The gray values of a sub-window are projected onto a line, and sum the gray values which are projected onto one same point to shape a special vector, then rotate the projecting direction, obtain many such vectors corresponding to different projecting directions. The vectors can form a matrix, a GPI matrix of the sub-image. The problem of lines detection is converted into maxima or minima searching problem in the GPI matrix. Bused on the GPI matrix, the lines can be calculated. Different from traditional methods, the algorithm can detect the positions of lines accurately, quickly without previous edge detection, which costs less time, and avoids the error resulted from the poor threshold with traditional methods. This algorithm is useful and efftcient for numerous image understanding applications and robot visual navigation, especially for welded joint position detection in heavy noise.展开更多
There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods...There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.展开更多
Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabi...Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.展开更多
Computer vision has come into used in the fields of welding process control and automation. In order to improve precision and rapidity of welding image processing, a novel method based on fractal theory has been put f...Computer vision has come into used in the fields of welding process control and automation. In order to improve precision and rapidity of welding image processing, a novel method based on fractal theory has been put forward in this paper. Compared with traditional methods, the image is preliminarily processed in the macroscopic regions then thoroughly analyzed in the microscopic regions in the new method. With which, an image is divided up to some regions according to the different fractal characters of image edge, and the fuzzy regions including image edges are detected out, then image edges are identified with Sobel operator and curved by LSM (Lease Square Method). Since the data to be processed have been decreased and the noise of image has been reduced, it has been testified through experiments that edges of weld seam or weld pool could be recognized correctly and quickly.展开更多
As a commonly used non-contact flatness detection method, laser triangular detection method is designed with low cost, but it cannot avoid measurement errors caused by strip steel vibration effectively. This paper put...As a commonly used non-contact flatness detection method, laser triangular detection method is designed with low cost, but it cannot avoid measurement errors caused by strip steel vibration effectively. This paper puts forward a dynamic flatness image processing method based on improved laser triangular detection method. According to the practical application of strip steel straightening, it completes the image pre-processing, image feature curve extraction and calculation of flatness elongation using digital image processing technology. Finally it eliminates elongation measurement errors caused by the vibration.展开更多
A new algorithm is proposed to determine the actual length and the number of the protruding fibres of yarn based on a combination of image acquisition technology. First, a yarn hairiness image is obtained by the serie...A new algorithm is proposed to determine the actual length and the number of the protruding fibres of yarn based on a combination of image acquisition technology. First, a yarn hairiness image is obtained by the series of image processing procedures that include grayscale transformation,skew correction,yarn binary image acquisition and yarn core binary image obtaining. Then,the hairiness is realized in single pixel width by the usage of thinning algorithm. Finally, a baseline of yarn core margin is obtained,and pixels that match 8-neighbor template correctly are found by row scanning in a certain area. From this,these pixels are judged and the real crossover points of yarn core margin and hairiness,i. e.,the starting points of hairiness,are gained. The real length of the protruding fibres is calculated by tracking hairiness from the starting point constantly.展开更多
In this paper,shock train motion in a Mach number 2.7 duct is studied experimentally,and large numbers of schlieren images are obtained by a high-speed camera.An image processing method based on Maximum Correlation De...In this paper,shock train motion in a Mach number 2.7 duct is studied experimentally,and large numbers of schlieren images are obtained by a high-speed camera.An image processing method based on Maximum Correlation Detection(MCD)is proposed to detect shock train motion from the schlieren images,based on which the key structures,e.g.,separation positions and separation shock angles on the top and bottom walls,can be analysed in detail.The oscillations of the shock train are generated by rhombus and ellipse shafts at various excitation frequencies.According to the analysis of MCD results,the distributions of the frequency components of shock train oscillation generated by the two shafts are distinctly different,in which the motion generated by the ellipse shaft is much smoother;shock train motion is mainly characterized by the oscillation of separation position while the separation shock strength is not so sensitive to downstream disturbance;there is a hysteresis loop relation between the downstream pressure and separation position.展开更多
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...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.展开更多
The network infrastructure has evolved rapidly due to the everincreasing volume of users and data.The massive number of online devices and users has forced the network to transform and facilitate the operational neces...The network infrastructure has evolved rapidly due to the everincreasing volume of users and data.The massive number of online devices and users has forced the network to transform and facilitate the operational necessities of consumers.Among these necessities,network security is of prime significance.Network intrusion detection systems(NIDS)are among the most suitable approaches to detect anomalies and assaults on a network.However,keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the intruders.This paper presents an effective and prevalent framework for NIDS by merging image processing with convolution neural networks(CNN).The proposed framework first converts non-image data from network traffic into images and then further enhances those images by using the Gabor filter.The images are then classified using a CNN classifier.To assess the efficacy of the recommended method,four benchmark datasets i.e.,CSE-CIC-IDS2018,CIC-IDS-2017,ISCX-IDS 2012,and NSL-KDD were used.The proposed approach showed higher precision in contrast with the recent work on the mentioned datasets.Further,the proposed method is compared with the recent well-known image processing methods for NIDS.展开更多
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 e...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.展开更多
Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials.This paper presents a method for the automatic recognition of bubbles in transmissi...Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials.This paper presents a method for the automatic recognition of bubbles in transmission electron microscope(TEM)images of W nanofibers using image processing techniques and convolutional neural network(CNN).We employ a three-stage approach consisting of Otsu,local-threshold,and watershed segmentation to extract bubbles from noisy images.To address over-segmentation,we propose a combination of area factor and radial pixel intensity scanning.A CNN is used to recognize bubbles,outperforming traditional neural network models such as Alex Net and Google Net with an accuracy of 97.1%and recall of 98.6%.Our method is tested on both clear and blurred TEM images,and demonstrates humanlike performance in recognizing bubbles.This work contributes to the development of quantitative image analysis in the field of plasma-material interactions,offering a scalable solution for analyzing material defects.Overall,this study's findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions.This method can be employed in a variety of specialties,including plasma physics and materials science.展开更多
基金supported by National Natural Science Foundation of China No. 50705030Guangdong Province Foundation of No.0133002
文摘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.
基金The work was supported by National Natural Science Foundation of China （No. 50975195）.
文摘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.
文摘An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.
文摘Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.
文摘Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.
基金supported by the National Key Research and Development Program of China(2021YFF0700305 and 2018YFE0119000)the National Natural Science Foundation of China(22104129 and 62105288)+1 种基金Zhejiang Province Science and Technology Research Plan(2022C03014)the Fundamental Research Funds for the Central Universities(2021XZZX022)and Alibaba Cloud.
文摘Fluorescence microscopy has become an essential tool for biologists,to visualize the dynamics of intracellular structures with specific labeling.Quantitatively measuring the dynamics of moving objects inside the cell is pivotal for understanding of the underlying regulatory mechanism.Protein-containing vesicles are involved in various biological processes such as material transportation,organelle interaction,and hormonal regulation,whose dynamic characteristics are signi¯cant to disease diagnosis and drug screening.Although some algorithms have been developed for vesicle tracking,most of them have limited performance when dealing with images with low resolution,poor signal-to-noise ratio(SNR)and complicated motion.Here,we proposed a novel deep learning-based method for intracellular vesicle tracking.We trained the U-Net for vesicle localization and motion classification,with demonstrates great performance in both simulated datasets and real biological samples.By combination with fan-shaped tracker(FsT)we have previously developed,this hybrid new algorithm significantly improved the performance of particle tracking with the function of subsequently automated vesicle motion classification.Furthermore,its performance was further demonstrated in analyzing with vesicle dynamics in different temperature,which achieved reasonable outcomes.Thus,we anticipate that this novel method would have vast applications in analyzing the vesicle dynamics in living cells.
基金National Natural Science Foundation of China(No.61302159,61227003,61301259)Natual Science Foundation of Shanxi Province(No.2012021011-2)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education,China(No.20121420110006)Top Science and Technology Innovation Teams of Higher Learning Institutions of Shanxi Province,ChinaProject Sponsored by Scientific Research for the Returned Overseas Chinese Scholars,Shanxi Province(No.2013-083)
文摘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.
基金supported by the National Natural Science Foundation of China(grant No.51706109)the National Natural Science Foundation of China(grant No.52006108).
文摘A new image processing method based on the high-speed camera is proposed to identify,locate,and track clusters.The instantaneous characteristic parameters of particle clusters in the riser of the circu-lating fluidized bed(CFB)can be acquired,such as solids holdup,vertical velocity,lateral displacement,aspect ratio and near-circularity.Experiments were carried out with glass bead particles,river sand particles and FCC particles.The time series of images of gas-solid flow in a CFB riser with a 100 mm x 25 mm cross-section and 3.2 m in length were obtained using high-speed cameras.The k-means++clustering algorithm is utilized to identify the clusters,centroid is applied to locate the clusters,and the cross-correlation algorithm is employed to track the specific clusters and number them to get the instantaneous characteristic parameters.The results illustrate that the shapes of clusters in the center area are closest to circle,moving upwards at a uniform speed,while the clusters in the side-wall area are mostly elongated or long chain-like,moving slowly downwards.In the transition area,the clusters are more complex,moving upwards at a constant speed,and having large lateral displacement.The results show that the image processing method used in this study is successful in acquiring the dynamic and structural parameters of the clusters simultaneously.
基金financially supported by the National High Technology Research and Development Program of China (863 Program, 2013AA102402)the 521 Talent Project of Zhejiang Sci-Tech University, Chinathe Key Research and Development Program of Zhejiang Province, China (2015C03023)
文摘A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.
基金This research was funded by Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education, The Research Fund for the Doctoral Program of Higher Education （No. 20020003053）National Natural Science Foundation of China （ No. 50275083 ）.
文摘A new efftcient straight line detection algorithm, GPI （ Gray Projecting Integral） method is proposed. The gray values of a sub-window are projected onto a line, and sum the gray values which are projected onto one same point to shape a special vector, then rotate the projecting direction, obtain many such vectors corresponding to different projecting directions. The vectors can form a matrix, a GPI matrix of the sub-image. The problem of lines detection is converted into maxima or minima searching problem in the GPI matrix. Bused on the GPI matrix, the lines can be calculated. Different from traditional methods, the algorithm can detect the positions of lines accurately, quickly without previous edge detection, which costs less time, and avoids the error resulted from the poor threshold with traditional methods. This algorithm is useful and efftcient for numerous image understanding applications and robot visual navigation, especially for welded joint position detection in heavy noise.
基金National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2018A0303130188)+1 种基金Guangdong Provincial Science and Technology Special Funds Project of China(Grant No.190805145540361)Special Projects in Key Fields of Colleges and Universities in Guangdong Province of China(Grant No.2020ZDZX2005).
文摘There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.
基金Part of the research leading to these results has received funding from the research project DESDEMONA–Detection of Steel Defects by Enhanced MONitoring and Automated procedure for self-inspection and maintenance (grant agreement number RFCS-2018_800687) supported by EU Call RFCS-2017sponsored by the NATO Science for Peace and Security Programme under grant id. G5924。
文摘Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.
文摘Computer vision has come into used in the fields of welding process control and automation. In order to improve precision and rapidity of welding image processing, a novel method based on fractal theory has been put forward in this paper. Compared with traditional methods, the image is preliminarily processed in the macroscopic regions then thoroughly analyzed in the microscopic regions in the new method. With which, an image is divided up to some regions according to the different fractal characters of image edge, and the fuzzy regions including image edges are detected out, then image edges are identified with Sobel operator and curved by LSM (Lease Square Method). Since the data to be processed have been decreased and the noise of image has been reduced, it has been testified through experiments that edges of weld seam or weld pool could be recognized correctly and quickly.
文摘As a commonly used non-contact flatness detection method, laser triangular detection method is designed with low cost, but it cannot avoid measurement errors caused by strip steel vibration effectively. This paper puts forward a dynamic flatness image processing method based on improved laser triangular detection method. According to the practical application of strip steel straightening, it completes the image pre-processing, image feature curve extraction and calculation of flatness elongation using digital image processing technology. Finally it eliminates elongation measurement errors caused by the vibration.
基金National Natural Science Foundation of China(No.61301276)Xi’an Polytechnic University Young Scholar Backbone Supporting Plan,ChinaDiscipline Construction Funds of Xi’an Polytechnic University,China(No.107090811)
文摘A new algorithm is proposed to determine the actual length and the number of the protruding fibres of yarn based on a combination of image acquisition technology. First, a yarn hairiness image is obtained by the series of image processing procedures that include grayscale transformation,skew correction,yarn binary image acquisition and yarn core binary image obtaining. Then,the hairiness is realized in single pixel width by the usage of thinning algorithm. Finally, a baseline of yarn core margin is obtained,and pixels that match 8-neighbor template correctly are found by row scanning in a certain area. From this,these pixels are judged and the real crossover points of yarn core margin and hairiness,i. e.,the starting points of hairiness,are gained. The real length of the protruding fibres is calculated by tracking hairiness from the starting point constantly.
基金supported by the National Numerical Wind Tunnel Project of China,the National Natural Science Foundation of China(Nos.12002163 and 12072157)the Natural Science Foundation of Jiangsu Province,China(No.BK20200408)+1 种基金the China Postdoctoral Science Foundation(No.2022T150321)the Key Laboratory of Hypersonic Aerodynamic Force and Heat Technology,AVIC Aerodynamics Research Institute,China。
文摘In this paper,shock train motion in a Mach number 2.7 duct is studied experimentally,and large numbers of schlieren images are obtained by a high-speed camera.An image processing method based on Maximum Correlation Detection(MCD)is proposed to detect shock train motion from the schlieren images,based on which the key structures,e.g.,separation positions and separation shock angles on the top and bottom walls,can be analysed in detail.The oscillations of the shock train are generated by rhombus and ellipse shafts at various excitation frequencies.According to the analysis of MCD results,the distributions of the frequency components of shock train oscillation generated by the two shafts are distinctly different,in which the motion generated by the ellipse shaft is much smoother;shock train motion is mainly characterized by the oscillation of separation position while the separation shock strength is not so sensitive to downstream disturbance;there is a hysteresis loop relation between the downstream pressure and separation position.
文摘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.
基金This work was supported by the National Research Foundation of Korea(NRF)NRF-2022R1A2C1011774.
文摘The network infrastructure has evolved rapidly due to the everincreasing volume of users and data.The massive number of online devices and users has forced the network to transform and facilitate the operational necessities of consumers.Among these necessities,network security is of prime significance.Network intrusion detection systems(NIDS)are among the most suitable approaches to detect anomalies and assaults on a network.However,keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the intruders.This paper presents an effective and prevalent framework for NIDS by merging image processing with convolution neural networks(CNN).The proposed framework first converts non-image data from network traffic into images and then further enhances those images by using the Gabor filter.The images are then classified using a CNN classifier.To assess the efficacy of the recommended method,four benchmark datasets i.e.,CSE-CIC-IDS2018,CIC-IDS-2017,ISCX-IDS 2012,and NSL-KDD were used.The proposed approach showed higher precision in contrast with the recent work on the mentioned datasets.Further,the proposed method is compared with the recent well-known image processing methods for NIDS.
文摘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.
基金supported by the National Key R&D Program of China(No.2017YFE0300106)Dalian Science and Technology Star Project(No.2020RQ136)+1 种基金the Central Guidance on Local Science and Technology Development Fund of Liaoning Province(No.2022010055-JH6/100)the Fundamental Research Funds for the Central Universities(No.DUT21RC(3)066)。
文摘Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials.This paper presents a method for the automatic recognition of bubbles in transmission electron microscope(TEM)images of W nanofibers using image processing techniques and convolutional neural network(CNN).We employ a three-stage approach consisting of Otsu,local-threshold,and watershed segmentation to extract bubbles from noisy images.To address over-segmentation,we propose a combination of area factor and radial pixel intensity scanning.A CNN is used to recognize bubbles,outperforming traditional neural network models such as Alex Net and Google Net with an accuracy of 97.1%and recall of 98.6%.Our method is tested on both clear and blurred TEM images,and demonstrates humanlike performance in recognizing bubbles.This work contributes to the development of quantitative image analysis in the field of plasma-material interactions,offering a scalable solution for analyzing material defects.Overall,this study's findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions.This method can be employed in a variety of specialties,including plasma physics and materials science.
