Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marke...Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marker is important when quantifying frontal and transverse plane hip and knee kinematics,parameters which are particularly relevant to investigate in individuals with conditions such as patellofemoral pain,knee osteoarthritis,anterior cruciate ligament(ACL) injury,and hip pain.The aim of this study was to evaluate the effect of including the greater trochanter in the construction of the thigh segment on hip and knee kinematics during gait.Methods:3D kinematics were collected in 19 healthy subjects during walking using a surface marker system.Hip and knee angles were compared across two thigh segment definitions(with and without greater trochanter) at two time points during stance:peak knee flexion(PKF) and minimum knee flexion(Min KF).Results:Hip and knee angles differed in magnitude and direction in the transverse plane at both time points.In the thigh model with the greater trochanter the hip was more externally rotated than in the thigh model without the greater trochanter(PKF:-9.34°± 5.21° vs.1.40°± 5.22°,Min KF:-5.68°± 4.24° vs.5.01°± 4.86°;p < 0.001).In the thigh model with the greater trochanter,the knee angle was more internally rotated compared to the knee angle calculated using the thigh definition without the greater trochanter(PKF:14.67°± 6.78° vs.4.33°± 4.18°,Min KF:10.54°± 6.71° vs.-0.01°± 2.69°;p < 0.001).Small but significant differences were detected in the sagittal and frontal plane angles at both time points(p < 0.001).Conclusion:Hip and knee kinematics differed across different segment definitions including or excluding the greater trochanter marker,especially in the transverse plane.Therefore when considering whether to include the greater trochanter in the thigh segment model when using a surface markers to calculate 3D kinematics for movement assessment,it is important to have a clear understanding of the effect of different marker sets and segment models in use.展开更多
The quantitative analysis of dispersed phases(bubbles,droplets,and particles)in multiphase flow systems represents a persistent technological challenge in petroleum engineering applications,including CO2-enhanced oil ...The quantitative analysis of dispersed phases(bubbles,droplets,and particles)in multiphase flow systems represents a persistent technological challenge in petroleum engineering applications,including CO2-enhanced oil recovery,foam flooding,and unconventional reservoir development.Current characterization methods remain constrained by labor-intensive manual workflows and limited dynamic analysis capabilities,particularly for processing large-scale microscopy data and video sequences that capture critical transient behavior like gas cluster migration and droplet coalescence.These limitations hinder the establishment of robust correlations between pore-scale flow patterns and reservoir-scale production performance.This study introduces a novel computer vision framework that integrates foundation models with lightweight neural networks to address these industry challenges.Leveraging the segment anything model's zero-shot learning capability,we developed an automated workflow that achieves an efficiency improvement of approximately 29 times in bubble labeling compared to manual methods while maintaining less than 2%deviation from expert annotations.Engineering-oriented optimization ensures lightweight deployment with 94%segmentation accuracy,while the integrated quantification system precisely resolves gas saturation,shape factors,and interfacial dynamics,parameters critical for optimizing gas injection strategies and predicting phase redistribution patterns.Validated through microfluidic gas-liquid displacement experiments for discontinuous phase segmentation accuracy,this methodology enables precise bubble morphology quantification with broad application potential in multiphase systems,including emulsion droplet dynamics characterization and particle transport behavior analysis.This work bridges the critical gap between pore-scale dynamics characterization and reservoir-scale simulation requirements,providing a foundational framework for intelligent flow diagnostics and predictive modeling in next-generation digital oilfield systems.展开更多
Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils,which remains one of the keys to optical remote sensi...Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils,which remains one of the keys to optical remote sensing of oil spills.Optical remotely sensed images of oil spills are inherently multidimensional and embedded with a complex knowledge framework.This complexity often hinders the effectiveness of mechanistic algorithms across varied scenarios.Although optical remote-sensing theory for oil spills has advanced,the scarcity of curated datasets and the difficulty of collecting them limit their usefulness for training deep learning models.This study introduces a data expansion strategy that utilizes the Segment Anything Model(SAM),effectively bridging the gap between traditional mechanism algorithms and emergent self-adaptive deep learning models.Optical dimension reduction is achieved through standardized preprocessing processes that address the decipherable properties of the input image.After preprocessing,SAM can swiftly and accurately segment spilled oil in images.The unified AI-based workflow significantly accelerates labeled-dataset creation and has proven effective for both rapid emergency intelligence during spill incidents and the rapid mapping and classification of oil footprints across China’s coastal waters.Our results show that coupling a remote sensing mechanism with a foundation model enables near-real-time,large-scale monitoring of complex surface slicks and offers guidance for the next generation of detection and quantification algorithms.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this stud...Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this study,both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery.The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR-NDVI image,whereas the coarse segmentation relied on the segment anything model(SAM).Both methods successfully delineated tree crown outlines,however,only the fine segmentation accurately captured internal canopy gaps.Despite these structural differences,mean NDVI values calculated per tree crown revealed no significant differences between the two approaches,indicating that coarse segmentation is sufficient for mean vegetation index assessments.Nevertheless,the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees,underscoring its value for detailed structural and health analyses.Furthermore,the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys.For applications focused on structural integrity and spatial variation in canopy health,the fine segmentation approach is recommended.展开更多
AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigat...AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet^(++))]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC.展开更多
Equilibrium points and periodic orbits in irregular gravitational fields are significant for an understanding of dynamical behaviors around asteroids as well as deep space exploring missions. The dipole segment is a g...Equilibrium points and periodic orbits in irregular gravitational fields are significant for an understanding of dynamical behaviors around asteroids as well as deep space exploring missions. The dipole segment is a good alternative model to study qualitative dynamical properties near dumbbell-shaped asteroids. In this paper, the dipole segment model and its equilibrium points are simply introduced. The stability of the two triangular equilibrium points of the system is numerically examined. Next, periodic orbits are presented around the dipole segment model in two different cases, in which triangular equilibria are linearly stable and unstable,respectively. New types of periodic orbits are illustrated in detail, including their orbital shapes, periods and the Jacobi integral.The orbital stability, topological classification and bifurcations of these orbits are also analyzed with numerical continuations.展开更多
Three model polyurethane hard segments based on dimethylol butanoic acid (DMBA) and 1,6-hexane diisocyanate (HDI), toluene diisocyanate (TDI) and 4,4'-diphenylmethane diisocyanate (MDI) were prepared by the soluti...Three model polyurethane hard segments based on dimethylol butanoic acid (DMBA) and 1,6-hexane diisocyanate (HDI), toluene diisocyanate (TDI) and 4,4'-diphenylmethane diisocyanate (MDI) were prepared by the solution method. Fourier Infrared (FTIR) spectroscopy was employed to study the H-bonds in these model polyurethanes. The model polyurethane hard segment prepared from HDI and 1,4-butanodiol (BDO) was used for comparison. It was found that the incorporation of the pendent carboxyl through DMBA into the model hard segments weakens the original NH…O = C H-bond but gives more H-bond patterns based on the two H-bond donors, urethane NH and carboxylic OH. The carboxylic dimer is one of the main H-bond types and is stronger than another main H-bond type NH…O=C. In addition, the H-bond in aromatic model hard segments is stronger than that of aliphatic hard segments. The appearance of the free C=O and the fact that almost all N—H is H-bonded suggest that there possibly exist either the third H-bond acceptor or the H-bond formed by one acceptor with two donors.展开更多
Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassificatio...Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassification. A new segmentation method, called multi-resolution Ganssian mixture model method, is proposed. First, an image pyramid is constructed and son-father link relationship is built between each level of pyramid. Then the mixture model segmentation method is applied to the top level. The segmentation result on the top level is passed top-down to the bottom level according to the son-father link relationship between levels. The proposed method considers not only local but also global information of image, it overcomes the effect of noise and can obtain better segmentation result. Experimental result demonstrates its effectiveness.展开更多
In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution....In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.展开更多
he objective of the research is to develop a fast procedure for segmenting typical videophone images. In this paper, a new approach to color image segmentation based on HSI(Hue, Saturation, Intensity) color model is r...he objective of the research is to develop a fast procedure for segmenting typical videophone images. In this paper, a new approach to color image segmentation based on HSI(Hue, Saturation, Intensity) color model is reported. It is in contrast to the conventional approaches by using the three components of HSI color model in succession. This strategy makes the segmentation procedure much fast and effective. Experimental results with typical “headandshoulders” real images taken from videophone sequences show that the new appproach can fulfill the application requirements.展开更多
This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The mos...This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.展开更多
Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in ord...Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.展开更多
Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaptio...Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaption parameter and modifying noisy data term, the proposed variational model provides a good solution for the coastal zone SAR image with common characteristics of inherent speckle noise and complicated geometrical details. However, the proposed model is difficult to solve due to to its nonlinear, non-convex and non-smooth characteristics. Followed by curve evolution theory and operator splitting method, the minimization problem is reformulated as a constrained minimization problem. A fast alternating minimization iterative scheme is designed to implement coastal zone segmentation. Finally, various two-stage and multiphase experimental results illustrate the advantage of the proposed segmentation model, and indicate the high computation efficiency of designed numerical approximation algorithm.展开更多
The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta...The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I<sub>1</sub>, I<sub>2</sub>, I<sub>3</sub>) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.展开更多
Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese m...Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per）brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established.展开更多
Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by link...Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.展开更多
This study develops crash rate prediction models based on the premise that crash frequencies observed from adjacent paired non-weaving and weaving freeway segments are spatially correlated and therefore requires a sim...This study develops crash rate prediction models based on the premise that crash frequencies observed from adjacent paired non-weaving and weaving freeway segments are spatially correlated and therefore requires a simultaneous equation modeling approach. Simultaneous equation models for paired freeway non-weaving segments and weaving segments along with combined three freeway segments upstream and downstream were developed to investigate the relationship of crash rate with freeway characteristics. The endogenous variables have significant coefficients which indicate that unobserved variables exist on these contiguous segments, resulting in different crash rates. AADT is a variable that can show the interaction between the traffic and crashes on these contiguous segments. The results corroborate such an interaction. By comparing the simultaneous equation model and the multiple linear regression model, it is shown that more model parameters in the simultaneous models are significant than those from linear regression model. This demonstrates the existence of the correlation between the interchange and between-interchange segments. It is crucial that some variables like segment length can be identified significant in the simultaneous model, which provides a way to quantify the safety impact of freeway development.展开更多
Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This ...Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model(SAM).By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters,a multispectral rock particle segmentation model named CoreSAM is constructed,which achieves rock particle edge extraction and type identification.Building upon this,we propose a comprehensive quantitative evaluation system for rock structure,assessing parameters including particle size,sorting,roundness,particle contact and cementation types.The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs.The proposed method enables full-sample,classified particle size analysis and quantitative characterization of parameters like roundness,advancing reservoir evaluation towards more precise,quantitative,intuitive,and comprehensive development.展开更多
To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively ap...To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.展开更多
基金the National Institute of Child Health and Human Development (No.NICHD,No.R15HD059080,and No.R15HD059080-01A1S1)
文摘Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marker is important when quantifying frontal and transverse plane hip and knee kinematics,parameters which are particularly relevant to investigate in individuals with conditions such as patellofemoral pain,knee osteoarthritis,anterior cruciate ligament(ACL) injury,and hip pain.The aim of this study was to evaluate the effect of including the greater trochanter in the construction of the thigh segment on hip and knee kinematics during gait.Methods:3D kinematics were collected in 19 healthy subjects during walking using a surface marker system.Hip and knee angles were compared across two thigh segment definitions(with and without greater trochanter) at two time points during stance:peak knee flexion(PKF) and minimum knee flexion(Min KF).Results:Hip and knee angles differed in magnitude and direction in the transverse plane at both time points.In the thigh model with the greater trochanter the hip was more externally rotated than in the thigh model without the greater trochanter(PKF:-9.34°± 5.21° vs.1.40°± 5.22°,Min KF:-5.68°± 4.24° vs.5.01°± 4.86°;p < 0.001).In the thigh model with the greater trochanter,the knee angle was more internally rotated compared to the knee angle calculated using the thigh definition without the greater trochanter(PKF:14.67°± 6.78° vs.4.33°± 4.18°,Min KF:10.54°± 6.71° vs.-0.01°± 2.69°;p < 0.001).Small but significant differences were detected in the sagittal and frontal plane angles at both time points(p < 0.001).Conclusion:Hip and knee kinematics differed across different segment definitions including or excluding the greater trochanter marker,especially in the transverse plane.Therefore when considering whether to include the greater trochanter in the thigh segment model when using a surface markers to calculate 3D kinematics for movement assessment,it is important to have a clear understanding of the effect of different marker sets and segment models in use.
基金supported by Sichuan Province Outstanding Young Scientist Fund(Grant No.2025NSFJQ0009)Sichuan Regional Innovation Cooperation Fund(Grant No.2025YFHZ0270)。
文摘The quantitative analysis of dispersed phases(bubbles,droplets,and particles)in multiphase flow systems represents a persistent technological challenge in petroleum engineering applications,including CO2-enhanced oil recovery,foam flooding,and unconventional reservoir development.Current characterization methods remain constrained by labor-intensive manual workflows and limited dynamic analysis capabilities,particularly for processing large-scale microscopy data and video sequences that capture critical transient behavior like gas cluster migration and droplet coalescence.These limitations hinder the establishment of robust correlations between pore-scale flow patterns and reservoir-scale production performance.This study introduces a novel computer vision framework that integrates foundation models with lightweight neural networks to address these industry challenges.Leveraging the segment anything model's zero-shot learning capability,we developed an automated workflow that achieves an efficiency improvement of approximately 29 times in bubble labeling compared to manual methods while maintaining less than 2%deviation from expert annotations.Engineering-oriented optimization ensures lightweight deployment with 94%segmentation accuracy,while the integrated quantification system precisely resolves gas saturation,shape factors,and interfacial dynamics,parameters critical for optimizing gas injection strategies and predicting phase redistribution patterns.Validated through microfluidic gas-liquid displacement experiments for discontinuous phase segmentation accuracy,this methodology enables precise bubble morphology quantification with broad application potential in multiphase systems,including emulsion droplet dynamics characterization and particle transport behavior analysis.This work bridges the critical gap between pore-scale dynamics characterization and reservoir-scale simulation requirements,providing a foundational framework for intelligent flow diagnostics and predictive modeling in next-generation digital oilfield systems.
基金The National Natural Science Foundation of China under contract No.42371380the National Key Research and Development Program of China under contract No.2023YFC2811800the Fundamental Research Funds for the Central Universities under contract No.0904-14380035.
文摘Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils,which remains one of the keys to optical remote sensing of oil spills.Optical remotely sensed images of oil spills are inherently multidimensional and embedded with a complex knowledge framework.This complexity often hinders the effectiveness of mechanistic algorithms across varied scenarios.Although optical remote-sensing theory for oil spills has advanced,the scarcity of curated datasets and the difficulty of collecting them limit their usefulness for training deep learning models.This study introduces a data expansion strategy that utilizes the Segment Anything Model(SAM),effectively bridging the gap between traditional mechanism algorithms and emergent self-adaptive deep learning models.Optical dimension reduction is achieved through standardized preprocessing processes that address the decipherable properties of the input image.After preprocessing,SAM can swiftly and accurately segment spilled oil in images.The unified AI-based workflow significantly accelerates labeled-dataset creation and has proven effective for both rapid emergency intelligence during spill incidents and the rapid mapping and classification of oil footprints across China’s coastal waters.Our results show that coupling a remote sensing mechanism with a foundation model enables near-real-time,large-scale monitoring of complex surface slicks and offers guidance for the next generation of detection and quantification algorithms.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金This study was conducted within the project FraxVir“Detection,characterisation and analyses of the occurrence of viruses and ash dieback in special stands of Fraxinus excelsior-a supplementary study to the FraxForFuture demonstration project”and receives funding via the Waldklimafonds(WKF)funded by the German Federal Ministry of Food and Agriculture(BMEL)and Federal Ministry for the Environment,Nature Conservation,Nuclear Safety and Consumer Protection(BMUV)administrated by the Agency for Renewable Resources(FNR)under grant agreement 2220WK40A4.
文摘Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this study,both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery.The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR-NDVI image,whereas the coarse segmentation relied on the segment anything model(SAM).Both methods successfully delineated tree crown outlines,however,only the fine segmentation accurately captured internal canopy gaps.Despite these structural differences,mean NDVI values calculated per tree crown revealed no significant differences between the two approaches,indicating that coarse segmentation is sufficient for mean vegetation index assessments.Nevertheless,the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees,underscoring its value for detailed structural and health analyses.Furthermore,the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys.For applications focused on structural integrity and spatial variation in canopy health,the fine segmentation approach is recommended.
基金Supported by the Shenzhen Science and Technology Program(No.JCYJ20240813152704006)the National Natural Science Foundation of China(No.62401259)+2 种基金the Fundamental Research Funds for the Central Universities(No.NZ2024036)the Postdoctoral Fellowship Program of CPSF(No.GZC20242228)High Performance Computing Platform of Nanjing University of Aeronautics and Astronautics。
文摘AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet^(++))]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC.
基金supported by the National Natural Science Foundation of China(Grant Nos.11602019&11572035)the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(Grant No.2016QNRC001)Excellent Young Teachers Program of Beijing Institute of Technology(Grant No.2015YG0605)
文摘Equilibrium points and periodic orbits in irregular gravitational fields are significant for an understanding of dynamical behaviors around asteroids as well as deep space exploring missions. The dipole segment is a good alternative model to study qualitative dynamical properties near dumbbell-shaped asteroids. In this paper, the dipole segment model and its equilibrium points are simply introduced. The stability of the two triangular equilibrium points of the system is numerically examined. Next, periodic orbits are presented around the dipole segment model in two different cases, in which triangular equilibria are linearly stable and unstable,respectively. New types of periodic orbits are illustrated in detail, including their orbital shapes, periods and the Jacobi integral.The orbital stability, topological classification and bifurcations of these orbits are also analyzed with numerical continuations.
基金This work was supported by the Natural Science Foundation of Henan Province (004030600)
文摘Three model polyurethane hard segments based on dimethylol butanoic acid (DMBA) and 1,6-hexane diisocyanate (HDI), toluene diisocyanate (TDI) and 4,4'-diphenylmethane diisocyanate (MDI) were prepared by the solution method. Fourier Infrared (FTIR) spectroscopy was employed to study the H-bonds in these model polyurethanes. The model polyurethane hard segment prepared from HDI and 1,4-butanodiol (BDO) was used for comparison. It was found that the incorporation of the pendent carboxyl through DMBA into the model hard segments weakens the original NH…O = C H-bond but gives more H-bond patterns based on the two H-bond donors, urethane NH and carboxylic OH. The carboxylic dimer is one of the main H-bond types and is stronger than another main H-bond type NH…O=C. In addition, the H-bond in aromatic model hard segments is stronger than that of aliphatic hard segments. The appearance of the free C=O and the fact that almost all N—H is H-bonded suggest that there possibly exist either the third H-bond acceptor or the H-bond formed by one acceptor with two donors.
基金This project was supported by the National Natural Foundation of China (60404022) and the Foundation of Department ofEducation of Hebei Province (2002209).
文摘Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassification. A new segmentation method, called multi-resolution Ganssian mixture model method, is proposed. First, an image pyramid is constructed and son-father link relationship is built between each level of pyramid. Then the mixture model segmentation method is applied to the top level. The segmentation result on the top level is passed top-down to the bottom level according to the son-father link relationship between levels. The proposed method considers not only local but also global information of image, it overcomes the effect of noise and can obtain better segmentation result. Experimental result demonstrates its effectiveness.
基金supported by the National Key R&D Program of China(2018YFC0309400)the National Natural Science Foundation of China(61871188)
文摘In many practical applications of image segmentation problems,employing prior information can greatly improve segmentation results.This paper continues to study one kind of prior information,called prior distribution.Within this research,there is no exact template of the object;instead only several samples are given.The proposed method,called the parametric distribution prior model,extends our previous model by adding the training procedure to learn the prior distribution of the objects.Then this paper establishes the energy function of the active contour model(ACM)with consideration of this parametric form of prior distribution.Therefore,during the process of segmenting,the template can update itself while the contour evolves.Experiments are performed on the airplane data set.Experimental results demonstrate the potential of the proposed method that with the information of prior distribution,the segmentation effect and speed can be both improved efficaciously.
文摘he objective of the research is to develop a fast procedure for segmenting typical videophone images. In this paper, a new approach to color image segmentation based on HSI(Hue, Saturation, Intensity) color model is reported. It is in contrast to the conventional approaches by using the three components of HSI color model in succession. This strategy makes the segmentation procedure much fast and effective. Experimental results with typical “headandshoulders” real images taken from videophone sequences show that the new appproach can fulfill the application requirements.
文摘This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020025Science and Technology Commissioner Program of Huzhou,Grant/Award Number:2023GZ42Sichuan Provincial Science and Technology Support Program,Grant/Award Numbers:2023ZHCG0005,2023ZHCG0008。
文摘Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.
基金supported by the China Postdoctoral Science Foundation under Grant No.2015M571993the Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MD004+1 种基金the National Natural Science Foundation of China under Grant No.61602269Qingdao Postdoctoral Application Research Funded Project
文摘Effective and efficient SAR image segmentation has a significant role in coastal zone interpretation. In this paper, a coastal zone segmentation model is proposed based on Potts model. By introducing edge self-adaption parameter and modifying noisy data term, the proposed variational model provides a good solution for the coastal zone SAR image with common characteristics of inherent speckle noise and complicated geometrical details. However, the proposed model is difficult to solve due to to its nonlinear, non-convex and non-smooth characteristics. Followed by curve evolution theory and operator splitting method, the minimization problem is reformulated as a constrained minimization problem. A fast alternating minimization iterative scheme is designed to implement coastal zone segmentation. Finally, various two-stage and multiphase experimental results illustrate the advantage of the proposed segmentation model, and indicate the high computation efficiency of designed numerical approximation algorithm.
文摘The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I<sub>1</sub>, I<sub>2</sub>, I<sub>3</sub>) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.
文摘Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per）brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established.
文摘Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.
文摘This study develops crash rate prediction models based on the premise that crash frequencies observed from adjacent paired non-weaving and weaving freeway segments are spatially correlated and therefore requires a simultaneous equation modeling approach. Simultaneous equation models for paired freeway non-weaving segments and weaving segments along with combined three freeway segments upstream and downstream were developed to investigate the relationship of crash rate with freeway characteristics. The endogenous variables have significant coefficients which indicate that unobserved variables exist on these contiguous segments, resulting in different crash rates. AADT is a variable that can show the interaction between the traffic and crashes on these contiguous segments. The results corroborate such an interaction. By comparing the simultaneous equation model and the multiple linear regression model, it is shown that more model parameters in the simultaneous models are significant than those from linear regression model. This demonstrates the existence of the correlation between the interchange and between-interchange segments. It is crucial that some variables like segment length can be identified significant in the simultaneous model, which provides a way to quantify the safety impact of freeway development.
基金Supported by the National Natural Science Foundation of China(42372175,72088101)PetroChina Science and Technology Project of(2023DJ84)Basic Research Cooperation Project between China National Petroleum Corporation and Peking University.
文摘Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model(SAM).By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters,a multispectral rock particle segmentation model named CoreSAM is constructed,which achieves rock particle edge extraction and type identification.Building upon this,we propose a comprehensive quantitative evaluation system for rock structure,assessing parameters including particle size,sorting,roundness,particle contact and cementation types.The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs.The proposed method enables full-sample,classified particle size analysis and quantitative characterization of parameters like roundness,advancing reservoir evaluation towards more precise,quantitative,intuitive,and comprehensive development.
文摘To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.
