The pore structure images of ore particles located at different heights of leaching column were scanned with X-ray computerized tomography (CT) scanner, the porosity and pore size distribution were calculated and the ...The pore structure images of ore particles located at different heights of leaching column were scanned with X-ray computerized tomography (CT) scanner, the porosity and pore size distribution were calculated and the geometrical shape and connectivity of pores were analyzed based on image process method, and the three dimensional reconstruction of pore structure images was realized. The results show that the porosity of ore particles bed in leaching column is 42.92%, 41.72%, 39.34% at top, middle and bottom zone, respectively. Obviously it has spatial variability and decreases appreciably along the height of the column. The overall average porosity obtained by image processing is 41.33% while the porosity gotten from general measurement method in laboratory is 42.77% showing the results of both methods are consistent well. The pore structure of ore granular media is characterized as a dynamical space network composed of interconnected pore bodies and pore throats. The ratio of throats with equivalent diameter less than 1.91 mm to the total pores is 29.31%, and that of the large pores with equivalent diameter more than 5.73 mm is 2.90%.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
The prerequisite for doctors to diagnose and treat patients is to be able to accurately determine the patient's physical condition, which requires the help of some medical image data to help doctors to judge. At p...The prerequisite for doctors to diagnose and treat patients is to be able to accurately determine the patient's physical condition, which requires the help of some medical image data to help doctors to judge. At present, many hospitals in China are equipped with more advanced and complete imaging equipment. The normal operation of these equipment is closely related to computer image processing technology. This technology is a new technology based on computer technology and biomedical technology. Its key role is to provide convenient conditions for doctors to carry out treatment scientifically and efficiently, and improve the comprehensive service level of the hospital. This paper analyzes and studies the progress and application of computer image processing technology in medical imaging.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
Objective: To study the effective computerized image processing of underexposed and overexposed X-rays of bones and joints. Methods: Ninety-nine conventional X-ray images (82 were overexposed and 17 were underexposed)...Objective: To study the effective computerized image processing of underexposed and overexposed X-rays of bones and joints. Methods: Ninety-nine conventional X-ray images (82 were overexposed and 17 were underexposed),scanned by an UMAX Astra 4000U Scanner, were converted into digital images on the basis of their analog images. A computerized imaging processing program consisting of five functional modules such as Contrast Stretch, Fast Flourier Transform (FFT), Image Smoothing Modules, Inverse Fast Flourier Transform (IFFT) and Nonlinear Transform performed image contrast stretch and smoothing. Three senior doctors from hospital image sections made their evaluation of all the processed images. Results: Of 82 overexposed films, 71 met the clinical requirements after image processing, and 11 were unable to be applied to clinical diagnosis, accounting for 87% and 13% respectively. Of the other 17 underexposed X-ray images, 11 met the clinical requirements while 6 were not, making a percentage of 64 and 35. Conclusion: Image contrast stretch and smoothing processing are significantly effective on conventional X-ray images which were inappropriately exposed, and can avoid more X-ray radiation caused by handling of radiological photograph again. This method can decrease hospital cost and provide acute and effective X-ray examinations for the treatment and cure for critical patients.展开更多
Using computer-controlled liquid crystal display (LCD) as an image processor and a CCD camera as a detector, phase-only correlation measurement is performed with the aid of joint transform correlation method (JTC). Th...Using computer-controlled liquid crystal display (LCD) as an image processor and a CCD camera as a detector, phase-only correlation measurement is performed with the aid of joint transform correlation method (JTC). This computer -controlled LCD-CCD image processing system may be a powerful tool for defect detection, position control and pattern recognition. It enables new possibilities in analog real-time image processing. This is of great interest in microelectronic manufacturing today and in the future.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
This study seeks to establish a novel,semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes,significantly...This study seeks to establish a novel,semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes,significantly contributing to the dependability of concrete quality evaluations.The study utilizes image processing and machine learning(ML)methods,namely object detectionmodels such as YOLOv8 and Convolutional Neural Networks(CNNs),to evaluate images of concrete cubes.These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions.Preliminary results indicate a good performance in the classification of concrete cube failure modes.The proposed system accurately identifies cracks,determines the severity of damage to structures,indicating the potential to minimize human errors and discrepancies that might occur through the current techniques to detect the failure mode of concrete cubes.Thedeveloped systemcould significantly improve the reliability of concrete cube assessments,reduce resource wastage,and contribute to more sustainable construction practices.By minimizing material costs and errors,this innovation supports the construction industry’s move towards sustainability.展开更多
In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide...In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions.However,micro milling tools have non-flat flanks,thin coatings can peel off,and spindle orientation is uncertain during downtime.These factors result in low pixel values,uneven illumination,and arbitrary tool position.To address this,we propose an image-based tool wear monitoring method.It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas.Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images,effectively addressing the effects of illumination and spindle orientation.Additionally,the algorithm has low complexity,fast execution time,and significantly reduces the detection time in situ.展开更多
All-optical image processing has been viewed as a promising technique for its high computation speed and low power consumption.However,current methods are often restricted to few functionalities and low reconfigurabil...All-optical image processing has been viewed as a promising technique for its high computation speed and low power consumption.However,current methods are often restricted to few functionalities and low reconfigurabilities,which cannot meet the growing demand for device integration and scenario adaptation in next-generation vision regimes.Here,we propose and experimentally demonstrate a bilayer liquid crystal computing platform for reconfigurable image processing.Under different in-situ/ex-situ twisted/untwisted conditions of the layers,our approach allows for eight kinds of image processing functions,including one/two-channel bright field imaging,one/two-channel vortex filtering,horizontally/vertically one-dimensional edge detection,vertex detection,and photonic spin Hall effect-based resolution adjustable edge detection.A unified theoretical framework for this scheme is established on the transfer function theory,which coincides well with the experimental results.The proposed method offers an easily-switchable multi-functional solution to optical image processing by introducing mechanical degrees of freedom,which may enable emerging applications in computer vision,autonomous driving,and biomedical microscopy.展开更多
This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert ...This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for decades.It is one of the most important research ...1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for decades.It is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,etc.Because image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image processing.More comprehensive description of the image is required to match the computational performance of current large scale models.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the qualit...In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the quality information. Abundant weld quality information is contained in weld pool and keyhole. Aiming at Nd：YAG laser welding of stainless steel, a coaxial visual sensing system was constructed. The images of weld pool and keyhole were obtained. Based on the gray character of weld pool and keyhole in images, an image processing algorithm was designed. The search start point and search criteria of weld pool and keyhole edge were determined respectively.展开更多
The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is present...The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is presented. It has many promising characteristics such as powerful computing capability, broad I/O bandwidth, topology flexibility, and expansibility. The parallel system performance is evaluated by practical experiment.展开更多
Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the m...Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the model,making it possible to apply models without any training.Therefore,we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic(PA)image processing.We employed the Segment Anything Model(SAM)by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks,including:(1)removing the skin signal in three-dimensional PA image rendering;(2)dual speed-of-sound reconstruction,and(3)segmentation ofnger blood vessels.Through these demonstrations,we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training.This potentially allows for a hands-on,convenient approach to achieving efficient and accurate segmentation of PA images.This paper serves as a comprehensive tutorial,facilitating the mastery of the technique through the provision of code and sample datasets.展开更多
Medical image processing technology plays an indispensable role in the field of modern medicine.By processing and analyzing medical images,it provides doctors with more comprehensive and accurate medical information,t...Medical image processing technology plays an indispensable role in the field of modern medicine.By processing and analyzing medical images,it provides doctors with more comprehensive and accurate medical information,thereby effectively aiding them in generating higher-quality treatment plans.In recent years,with the rapid development of deep learning technology,medical image processing techniques has been powered by providing more accurate information for diagnosis of disease.展开更多
Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learni...Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.展开更多
The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of re...The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.展开更多
基金Project(2004CB619205) supported by the National Key Fundamental Research and Development Program of ChinaProject(50325415) supported by the National Science Fund for Distinguished Young ScholarsProject(50574099) supported by the National Natural Science Foundation of China
文摘The pore structure images of ore particles located at different heights of leaching column were scanned with X-ray computerized tomography (CT) scanner, the porosity and pore size distribution were calculated and the geometrical shape and connectivity of pores were analyzed based on image process method, and the three dimensional reconstruction of pore structure images was realized. The results show that the porosity of ore particles bed in leaching column is 42.92%, 41.72%, 39.34% at top, middle and bottom zone, respectively. Obviously it has spatial variability and decreases appreciably along the height of the column. The overall average porosity obtained by image processing is 41.33% while the porosity gotten from general measurement method in laboratory is 42.77% showing the results of both methods are consistent well. The pore structure of ore granular media is characterized as a dynamical space network composed of interconnected pore bodies and pore throats. The ratio of throats with equivalent diameter less than 1.91 mm to the total pores is 29.31%, and that of the large pores with equivalent diameter more than 5.73 mm is 2.90%.
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.
文摘The prerequisite for doctors to diagnose and treat patients is to be able to accurately determine the patient's physical condition, which requires the help of some medical image data to help doctors to judge. At present, many hospitals in China are equipped with more advanced and complete imaging equipment. The normal operation of these equipment is closely related to computer image processing technology. This technology is a new technology based on computer technology and biomedical technology. Its key role is to provide convenient conditions for doctors to carry out treatment scientifically and efficiently, and improve the comprehensive service level of the hospital. This paper analyzes and studies the progress and application of computer image processing technology in medical imaging.
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.
文摘Objective: To study the effective computerized image processing of underexposed and overexposed X-rays of bones and joints. Methods: Ninety-nine conventional X-ray images (82 were overexposed and 17 were underexposed),scanned by an UMAX Astra 4000U Scanner, were converted into digital images on the basis of their analog images. A computerized imaging processing program consisting of five functional modules such as Contrast Stretch, Fast Flourier Transform (FFT), Image Smoothing Modules, Inverse Fast Flourier Transform (IFFT) and Nonlinear Transform performed image contrast stretch and smoothing. Three senior doctors from hospital image sections made their evaluation of all the processed images. Results: Of 82 overexposed films, 71 met the clinical requirements after image processing, and 11 were unable to be applied to clinical diagnosis, accounting for 87% and 13% respectively. Of the other 17 underexposed X-ray images, 11 met the clinical requirements while 6 were not, making a percentage of 64 and 35. Conclusion: Image contrast stretch and smoothing processing are significantly effective on conventional X-ray images which were inappropriately exposed, and can avoid more X-ray radiation caused by handling of radiological photograph again. This method can decrease hospital cost and provide acute and effective X-ray examinations for the treatment and cure for critical patients.
基金National Studying Abroad Foundation Management Commission of China!(No. 98822014)
文摘Using computer-controlled liquid crystal display (LCD) as an image processor and a CCD camera as a detector, phase-only correlation measurement is performed with the aid of joint transform correlation method (JTC). This computer -controlled LCD-CCD image processing system may be a powerful tool for defect detection, position control and pattern recognition. It enables new possibilities in analog real-time image processing. This is of great interest in microelectronic manufacturing today and in the future.
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.
文摘This study seeks to establish a novel,semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes,significantly contributing to the dependability of concrete quality evaluations.The study utilizes image processing and machine learning(ML)methods,namely object detectionmodels such as YOLOv8 and Convolutional Neural Networks(CNNs),to evaluate images of concrete cubes.These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions.Preliminary results indicate a good performance in the classification of concrete cube failure modes.The proposed system accurately identifies cracks,determines the severity of damage to structures,indicating the potential to minimize human errors and discrepancies that might occur through the current techniques to detect the failure mode of concrete cubes.Thedeveloped systemcould significantly improve the reliability of concrete cube assessments,reduce resource wastage,and contribute to more sustainable construction practices.By minimizing material costs and errors,this innovation supports the construction industry’s move towards sustainability.
基金Supported by National Natural Science Foundation of China(Grant No.52175528)。
文摘In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions.However,micro milling tools have non-flat flanks,thin coatings can peel off,and spindle orientation is uncertain during downtime.These factors result in low pixel values,uneven illumination,and arbitrary tool position.To address this,we propose an image-based tool wear monitoring method.It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas.Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images,effectively addressing the effects of illumination and spindle orientation.Additionally,the algorithm has low complexity,fast execution time,and significantly reduces the detection time in situ.
基金supported in part by the National Natural Science Foundation of China(12421005,12374273,and 61805077)in part by the Natural Science Foundation of Hunan Province(2025JJ50046)in part by the Hunan Provincial Major Sci-Tech Program(2023ZJ1010)。
文摘All-optical image processing has been viewed as a promising technique for its high computation speed and low power consumption.However,current methods are often restricted to few functionalities and low reconfigurabilities,which cannot meet the growing demand for device integration and scenario adaptation in next-generation vision regimes.Here,we propose and experimentally demonstrate a bilayer liquid crystal computing platform for reconfigurable image processing.Under different in-situ/ex-situ twisted/untwisted conditions of the layers,our approach allows for eight kinds of image processing functions,including one/two-channel bright field imaging,one/two-channel vortex filtering,horizontally/vertically one-dimensional edge detection,vertex detection,and photonic spin Hall effect-based resolution adjustable edge detection.A unified theoretical framework for this scheme is established on the transfer function theory,which coincides well with the experimental results.The proposed method offers an easily-switchable multi-functional solution to optical image processing by introducing mechanical degrees of freedom,which may enable emerging applications in computer vision,autonomous driving,and biomedical microscopy.
基金supported from the Strategic Pioneer Program of the Astronomy Large-Scale Scientific FacilityChinese Academy of Sciences and the Science and Education Integration Funding of University of Chinese Academy of Sciences+9 种基金the supports from the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the supports from the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No.XDB0550000the National Natural Science Foundation of China(NSFC,grant Nos.12422303 and12261141690)the supports from the NSFC(grant No.12403024)supports from the NSFC through grant Nos.11988101 and 11933004the Postdoctoral Fellowship Program of CPSF under grant No.GZB20240731the Young Data Scientist Project of the National Astronomical Data Centerthe China Post-doctoral Science Foundation(No.2023M743447)supports from the New Cornerstone Science Foundation through the New Cornerstone Investigator Program and the XPLORER PRIZE。
文摘This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
基金supported by 2023 Key Supported Project of the 14th Five Year Plan for Education and Science in Hunan Province with No.XJK23AXX0012021 Supported Project of the Educational Science Plan in Hunan Province with No.XJK21BXX010.
文摘1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for decades.It is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,etc.Because image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image processing.More comprehensive description of the image is required to match the computational performance of current large scale models.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金Project (10776020) supported by the Joint Foundation of the National Natural Science Foundation of China and China Academy of Engineering Physics
文摘In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the quality information. Abundant weld quality information is contained in weld pool and keyhole. Aiming at Nd：YAG laser welding of stainless steel, a coaxial visual sensing system was constructed. The images of weld pool and keyhole were obtained. Based on the gray character of weld pool and keyhole in images, an image processing algorithm was designed. The search start point and search criteria of weld pool and keyhole edge were determined respectively.
基金This project was supported by the National Natural Science Foundation of China (60135020).
文摘The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is presented. It has many promising characteristics such as powerful computing capability, broad I/O bandwidth, topology flexibility, and expansibility. The parallel system performance is evaluated by practical experiment.
基金support from Strategic Project of Precision Surgery,Tsinghua UniversityInitiative Scientific Research Program,Institute for Intelligent Healthcare,Tsinghua University+5 种基金Tsinghua-Foshan Institute of Advanced ManufacturingNational Natural Science Foundation of China(61735016)Beijing Nova Program(20230484308)Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)Youth Elite Program of Beijing Friendship Hospital(YYQCJH2022-9)Science and Technology Program of Beijing Tongzhou District(KJ2023CX012).
文摘Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the model,making it possible to apply models without any training.Therefore,we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic(PA)image processing.We employed the Segment Anything Model(SAM)by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks,including:(1)removing the skin signal in three-dimensional PA image rendering;(2)dual speed-of-sound reconstruction,and(3)segmentation ofnger blood vessels.Through these demonstrations,we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training.This potentially allows for a hands-on,convenient approach to achieving efficient and accurate segmentation of PA images.This paper serves as a comprehensive tutorial,facilitating the mastery of the technique through the provision of code and sample datasets.
文摘Medical image processing technology plays an indispensable role in the field of modern medicine.By processing and analyzing medical images,it provides doctors with more comprehensive and accurate medical information,thereby effectively aiding them in generating higher-quality treatment plans.In recent years,with the rapid development of deep learning technology,medical image processing techniques has been powered by providing more accurate information for diagnosis of disease.
基金the Natural Science Foundation of Zhejiang Province(No.LQ20F020024)。
文摘Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.
文摘The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.
