Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights t...Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.展开更多
In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularl...In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.展开更多
As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images...As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.展开更多
This study investigated the correlations between mechanical properties and mineralogy of granite using the digital image processing(DIP) and discrete element method(DEM). The results showed that the X-ray diffraction(...This study investigated the correlations between mechanical properties and mineralogy of granite using the digital image processing(DIP) and discrete element method(DEM). The results showed that the X-ray diffraction(XRD)-based DIP method effectively analyzed the mineral composition contents and spatial distributions of granite. During the particle flow code(PFC2D) model calibration phase, the numerical simulation exhibited that the uniaxial compressive strength(UCS) value, elastic modulus(E), and failure pattern of the granite specimen in the UCS test were comparable to the experiment. By establishing 351 sets of numerical models and exploring the impacts of mineral composition on the mechanical properties of granite, it indicated that there was no negative correlation between quartz and feldspar for UCS, tensile strength(σ_(t)), and E. In contrast, mica had a significant negative correlation for UCS, σ_(t), and E. The presence of quartz increased the brittleness of granite, whereas the presence of mica and feldspar increased its ductility in UCS and direct tensile strength(DTS) tests. Varying contents of major mineral compositions in granite showed minor influence on the number of cracks in both UCS and DTS tests.展开更多
A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-effi...A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.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
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
Numerical simulation is the most powerful computational and analysis tool for a large variety of engineering and physical problems.For a complex problem relating to multi-field,multi-process and multi-scale,different ...Numerical simulation is the most powerful computational and analysis tool for a large variety of engineering and physical problems.For a complex problem relating to multi-field,multi-process and multi-scale,different computing tools have to be developed so as to solve particular fields at different scales and for different processes.Therefore,the integration of different types of software is inevitable.However,it is difficult to perform the transfer of the meshes and simulated results among software packages because of the lack of shared data formats or encrypted data formats.An image processing based method for three-dimensional model reconstruction for numerical simulation was proposed,which presents a solution to the integration problem by a series of slice or projection images obtained by the post-processing modules of the numerical simulation software.By means of mapping image pixels to meshes of either finite difference or finite element models,the geometry contour can be extracted to export the stereolithography model.The values of results,represented by color,can be deduced and assigned to the meshes.All the models with data can be directly or indirectly integrated into other software as a continued or new numerical simulation.The three-dimensional reconstruction method has been validated in numerical simulation of castings and case studies were provided in this study.展开更多
Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely used...Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks.展开更多
In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and...In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and human factors involved in the production line some deficiencies may occur in product. It is important to detect such problems as early as possible. Surface defects and dimensional deviations are the most important quality problems. In this study, it is aimed to develop an approach to measure the dimensions of metal profiles by obtaining images of them. This will be of use in detecting the deviations in dimensions. A platform was introduced to simulate the real-time environment and images were taken from the metal profile using 4 laser light sources. The shape of the material is generated by combining the images taken from different cameras. Real dimensions were obtained by using image processing and mathematical conversion operations on the images. The results obtained with small deviations from the real values showed that this method can be applied in a real-time production line.展开更多
A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancella...A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancellation;(3) Information fusion of multi-spectral images and spot panchromatic images. The software experiments verify and evaluate the effectiveness and accuracy of the proposed algorithm.展开更多
The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disast...The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).展开更多
Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual conta...Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.展开更多
The dipping process was recorded firstly by high-speed camera system; acceleration time, speed, and dipping time were set by the control system of dipping bed, respectively. By image processing of dipping process base...The dipping process was recorded firstly by high-speed camera system; acceleration time, speed, and dipping time were set by the control system of dipping bed, respectively. By image processing of dipping process based on Otsu's method, it was found that low-viscosity flux glue eliminates the micelle effectively, very low speed also leads to small micelle hidden between the bumps, and this small micelle and hidden phenomenon disappeared when the speed is ≥0.2 cm s-1. Dipping flux quantity of the bump decreases by about 100 square pixels when flux viscosity is reduced from4,500 to 3,500 mpa s. For the 3,500 mpa s viscosity glue, dipping flux quantity increases with the increase of the speed and decreases with the increase of the speed after the speed is up to 0.8 cm s-1. The stable time of dipping glue can be obtained by real-time curve of dipping flux quantity and is only 80–90 ms when dipping speed is from 1.6 to 4.0 cm s-1. Dipping flux quantity has an increasing trend for acceleration time and has a decreasing trend for acceleration. Dipping flux quantity increases with the increase of dipping time, and is becoming saturated when the time is ≥55 ms.展开更多
In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers a...In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.展开更多
Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,...Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal.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
We present an improved digital image processing(DIP)method to calculate the widths of single slits.Different from the traditional laser Fraunhofer diffraction experiment in college physical experiments,by performing f...We present an improved digital image processing(DIP)method to calculate the widths of single slits.Different from the traditional laser Fraunhofer diffraction experiment in college physical experiments,by performing fast Fourier transform,inverse fast Fourier transform and the nonlinear leastsquare fitting on the diffraction pattern taken by a camera,the DIP method can quickly return an analytic expression,whose period is used to calculate widths of single slits.By comparing the measured results by the DIP method and the successional difference(SD)method,we find that for a single slit whose width is 60372μm,the DIP method is more accurate.Experimental results show that for single slits with widths between 40μm and 160μm,the relative error of the DIP method is less than 2.78%.Also,the DIP method can be used to measure the diameter of filament and fibres online in real time.展开更多
We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM...We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.展开更多
文摘Real-time capabilities and computational efficiency are provided by parallel image processing utilizing OpenMP. However, race conditions can affect the accuracy and reliability of the outcomes. This paper highlights the importance of addressing race conditions in parallel image processing, specifically focusing on color inverse filtering using OpenMP. We considered three solutions to solve race conditions, each with distinct characteristics: #pragma omp atomic: Protects individual memory operations for fine-grained control. #pragma omp critical: Protects entire code blocks for exclusive access. #pragma omp parallel sections reduction: Employs a reduction clause for safe aggregation of values across threads. Our findings show that the produced images were unaffected by race condition. However, it becomes evident that solving the race conditions in the code makes it significantly faster, especially when it is executed on multiple cores.
文摘In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.
文摘As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.
基金This research was supported by the Department of Mining Engineering at the University of Utah.In addition,the lead author wishes to acknowledge the financial support received from the Talent Introduction Project,part of the Elite Program of Shandong University of Science and Technology(No.0104060540171).
文摘This study investigated the correlations between mechanical properties and mineralogy of granite using the digital image processing(DIP) and discrete element method(DEM). The results showed that the X-ray diffraction(XRD)-based DIP method effectively analyzed the mineral composition contents and spatial distributions of granite. During the particle flow code(PFC2D) model calibration phase, the numerical simulation exhibited that the uniaxial compressive strength(UCS) value, elastic modulus(E), and failure pattern of the granite specimen in the UCS test were comparable to the experiment. By establishing 351 sets of numerical models and exploring the impacts of mineral composition on the mechanical properties of granite, it indicated that there was no negative correlation between quartz and feldspar for UCS, tensile strength(σ_(t)), and E. In contrast, mica had a significant negative correlation for UCS, σ_(t), and E. The presence of quartz increased the brittleness of granite, whereas the presence of mica and feldspar increased its ductility in UCS and direct tensile strength(DTS) tests. Varying contents of major mineral compositions in granite showed minor influence on the number of cracks in both UCS and DTS tests.
基金funded by the National Natural Science Foundation of China(41971226,41871357)the Major Research and Development and Achievement Transformation Projects of Qinghai,China(2022-QY-224)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28110502,XDA19030303).
文摘A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.
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.
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.
基金funded by National Key R&D Program of China(No.2021YFB3401200)the National Natural Science Foundation of China(No.51875308)the Beijing Nature Sciences Fund-Haidian Originality Cooperation Project(L212002).
文摘Numerical simulation is the most powerful computational and analysis tool for a large variety of engineering and physical problems.For a complex problem relating to multi-field,multi-process and multi-scale,different computing tools have to be developed so as to solve particular fields at different scales and for different processes.Therefore,the integration of different types of software is inevitable.However,it is difficult to perform the transfer of the meshes and simulated results among software packages because of the lack of shared data formats or encrypted data formats.An image processing based method for three-dimensional model reconstruction for numerical simulation was proposed,which presents a solution to the integration problem by a series of slice or projection images obtained by the post-processing modules of the numerical simulation software.By means of mapping image pixels to meshes of either finite difference or finite element models,the geometry contour can be extracted to export the stereolithography model.The values of results,represented by color,can be deduced and assigned to the meshes.All the models with data can be directly or indirectly integrated into other software as a continued or new numerical simulation.The three-dimensional reconstruction method has been validated in numerical simulation of castings and case studies were provided in this study.
基金supported by the National Natural Science Foundation of China under grants U1836208,by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)fund,China.
文摘Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks.
文摘In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and human factors involved in the production line some deficiencies may occur in product. It is important to detect such problems as early as possible. Surface defects and dimensional deviations are the most important quality problems. In this study, it is aimed to develop an approach to measure the dimensions of metal profiles by obtaining images of them. This will be of use in detecting the deviations in dimensions. A platform was introduced to simulate the real-time environment and images were taken from the metal profile using 4 laser light sources. The shape of the material is generated by combining the images taken from different cameras. Real dimensions were obtained by using image processing and mathematical conversion operations on the images. The results obtained with small deviations from the real values showed that this method can be applied in a real-time production line.
文摘A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancellation;(3) Information fusion of multi-spectral images and spot panchromatic images. The software experiments verify and evaluate the effectiveness and accuracy of the proposed algorithm.
基金funded by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,under grant No.(PNURSP2022R161).
文摘The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).
文摘Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.
基金supported by National Natural Science Foundation of China (No. 51275536)the China High Technology R&D Program 973 (No. 2015CB057206)
文摘The dipping process was recorded firstly by high-speed camera system; acceleration time, speed, and dipping time were set by the control system of dipping bed, respectively. By image processing of dipping process based on Otsu's method, it was found that low-viscosity flux glue eliminates the micelle effectively, very low speed also leads to small micelle hidden between the bumps, and this small micelle and hidden phenomenon disappeared when the speed is ≥0.2 cm s-1. Dipping flux quantity of the bump decreases by about 100 square pixels when flux viscosity is reduced from4,500 to 3,500 mpa s. For the 3,500 mpa s viscosity glue, dipping flux quantity increases with the increase of the speed and decreases with the increase of the speed after the speed is up to 0.8 cm s-1. The stable time of dipping glue can be obtained by real-time curve of dipping flux quantity and is only 80–90 ms when dipping speed is from 1.6 to 4.0 cm s-1. Dipping flux quantity has an increasing trend for acceleration time and has a decreasing trend for acceleration. Dipping flux quantity increases with the increase of dipping time, and is becoming saturated when the time is ≥55 ms.
基金supported by the National Key R&D Program of China(2017YFF0205600)the International Research Cooperation Seed Fund of Beijing University of Technology(2018A08)+1 种基金Science and Technology Project of Beijing Municipal Commission of Transport(2018-kjc-01-213)the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds(Scientific Research Categories)of Beijing City(PXM2019_014204_500032).
文摘In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
文摘Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal.
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National Natural Science Foundtion of China(No.11435011)Young Teachers Fund of Nanjing Institute of Technology,China(Nos.QKJ201907 and QKJ201908)+2 种基金China Scholarship Council(No.201708320319)Postgraduate Research and Practice Innovation Program of Jiangsu Province,China(No.KYZZ16-0349)Qing Lan Project of Jiangsu Province,China。
文摘We present an improved digital image processing(DIP)method to calculate the widths of single slits.Different from the traditional laser Fraunhofer diffraction experiment in college physical experiments,by performing fast Fourier transform,inverse fast Fourier transform and the nonlinear leastsquare fitting on the diffraction pattern taken by a camera,the DIP method can quickly return an analytic expression,whose period is used to calculate widths of single slits.By comparing the measured results by the DIP method and the successional difference(SD)method,we find that for a single slit whose width is 60372μm,the DIP method is more accurate.Experimental results show that for single slits with widths between 40μm and 160μm,the relative error of the DIP method is less than 2.78%.Also,the DIP method can be used to measure the diameter of filament and fibres online in real time.
基金supported by the National Key Research and Development Program of China(No.2016YFC1100600)the National Nature Science Foundation of China(Nos.61540006,61672363).
文摘We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.
