A Restricted, Adaptive Threshold Segmentation Approach for Processing High-Speed Image Sequences of the Glottis

In this paper, we propose a restricted, adaptive threshold approach for the segmentation of images of the glottis acquired from high speed video-endoscopy (HSV). The approach involves first, identifying a region of interest (ROI) that encloses the vocal-fold motion extent for each image frame as estimated by the different image sequences. This procedure is then followed by threshold segmentation restricted within the identified ROI for each image frame of the original image sequences, or referred to as sub-image sequences. The threshold value is adapted for each sub-image frame and determined by respective minimum gray-scale value that typically corresponds to a spatial location within the glottis. The proposed approach is practical and highly efficient for segmenting a vast amount of image frames since simple threshold method is adapted. Results obtained from the segmentation of representative clinical image sequences are presented to verify the proposed method.

A Versatile High-speed Image Processing System Based on DSP and CPLD

In this paper, we present an optimized design method for high-speed embedded image processing system using 32 bit floating-point Digital Signal Processor （DSP） and Complex Programmable Logic Device （CPLD）. The DSP acts as the main processor of the system： executes digital image processing algorithms and operates other devices such as image sensor and CPLD. The CPLD is used to acquire images and achieve complex logic control of the whole system. Some key technologies are introduced to enhance the performance of our system. In particular, the use of DSP/BIOS tool to develop DSP applications makes our program run much more efficiently. As a result, this system can provide an excellent computing platform not only for executing complex image processing algorithms, but also for other digital signal processing or multi-channel data collection by choosing different sensors or Analog-to-Digital （A/D） converters.

High-speed image reconstruction for optically sectioned,super-resolution structured illumination microscopy

Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achieve rapid,optically sectioned,SR observation with hundreds to thousands of time points.However,real-time observation is still out of reach for most SIM setups as conventional algorithms for image reconstruction involve a heavy computing burden.To address this limitation,an accelerated reconstruction algorithm was developed by implementing a simplified workflow for SR-SIM,termed joint space and frequency reconstruction.This algorithm results in an 80-fold improvement in reconstruction speed relative to the widely used Wiener-SIM.Critically,the increased processing speed does not come at the expense of spatial resolution or sectioning capability,as demonstrated by live imaging of microtubule dynamics and mitochondrial tubulation.

A novel detection method for warhead fragment targets in optical images under dynamic strong interference environments

A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.

基于手机拍照结合Image J软件对干辣椒外观品质的分级研究

干辣椒外观形状和色泽是其品质分类的重要指标。目前GB 10465—1989《辣椒干》中对干辣椒外观形状和色泽的检测方式还停留在人工检测阶段,通常受到主观感知、误差、视觉生理等多种因素影响,未形成科学标准化的检测方法。该研究利用手机拍照对干辣椒获取图像,通过Image J软件进行图像处理,提出了一种便捷、快速、准确的干辣椒外观形状相关特征量的测定方法。与游标卡尺法、剪纸法等人工测量相比,该方法更方便快速,可用于干辣椒的长度、宽度、面积等表型指标的测量。同时,通过构建红绿蓝(RGB)色彩模型获得干辣椒的外观颜色特征参数,色泽分选采用R/(G+B)比率为分级依据,结合干辣椒宽长比和面积可以将干辣椒分为优质、合格、不合格3个等级。

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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.

From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks

The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.

Quantum color image encryption: Dual scrambling scheme based on DNA codec and quantum Arnold transform

In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform(QAr T) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QAr T to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of “Monkey”, “Flower”, and “House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate(NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity(UACI) is 33.41%,and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

Microstructure effect of mechanical and cracking behaviors on brittle rocks using image-based fast Fourier transform method

The internal microstructures of rock materials, including mineral heterogeneity and intrinsic microdefects, exert a significant influence on their nonlinear mechanical and cracking behaviors. It is of great significance to accurately characterize the actual microstructures and their influence on stress and damage evolution inside the rocks. In this study, an image-based fast Fourier transform (FFT) method is developed for reconstructing the actual rock microstructures by combining it with the digital image processing (DIP) technique. A series of experimental investigations were conducted to acquire information regarding the actual microstructure and the mechanical properties. Based on these experimental evidences, the processed microstructure information, in conjunction with the proposed micromechanical model, is incorporated into the numerical calculation. The proposed image-based FFT method was firstly validated through uniaxial compression tests. Subsequently, it was employed to predict and analyze the influence of microstructure on macroscopic mechanical behaviors, local stress distribution and the internal crack evolution process in brittle rocks. The distribution of feldspar is considerably more heterogeneous and scattered than that of quartz, which results in a greater propensity for the formation of cracks in feldspar. It is observed that initial cracks and new cracks, including intragranular and boundary ones, ultimately coalesce and connect as the primary through cracks, which are predominantly distributed along the boundary of the feldspar. This phenomenon is also predicted by the proposed numerical method. The results indicate that the proposed numerical method provides an effective approach for analyzing, understanding and predicting the nonlinear mechanical and cracking behaviors of brittle rocks by taking into account the actual microstructure characteristics.

Dipping Process Characteristics Based on Image Processing of Pictures Captured by High-speed Cameras

The dipping process was recorded firstly by high-speed camera system; acceleration time, speed, and dipping time were set by the control system of dipping bed, respectively. By image processing of dipping process based on Otsu's method, it was found that low-viscosity flux glue eliminates the micelle effectively, very low speed also leads to small micelle hidden between the bumps, and this small micelle and hidden phenomenon disappeared when the speed is ≥0.2 cm s-1. Dipping flux quantity of the bump decreases by about 100 square pixels when flux viscosity is reduced from4,500 to 3,500 mpa s. For the 3,500 mpa s viscosity glue, dipping flux quantity increases with the increase of the speed and decreases with the increase of the speed after the speed is up to 0.8 cm s-1. The stable time of dipping glue can be obtained by real-time curve of dipping flux quantity and is only 80–90 ms when dipping speed is from 1.6 to 4.0 cm s-1. Dipping flux quantity has an increasing trend for acceleration time and has a decreasing trend for acceleration. Dipping flux quantity increases with the increase of dipping time, and is becoming saturated when the time is ≥55 ms.

Experimental Investigation of Flame Dynamics Based on High-Speed Images in Swirl Combustion Systems

The interaction mechanism of internally-staged-swirling stratified flame is complex,and the pilot flame has a manifest influence on flame stability.To study this,a series of experimental investigations for the pilot flame has been carried out in a model swirl combustor by only supplying the pilot fuel.The CH*chemiluminescence images of the pilot flame are acquired by a high-speed camera with a CH*bandpass filter,whose dynamic characteristics are identified by image statistical analysis and proper orthogonal decomposition(POD)analysis.And the fast algorithm based on matrix theory proposed in this paper increases the operation efficiency and operability of POD.With the pilot equivalence ratioΦincrease,the pilot flame gradually shows an unstable state,whose POD energy distribution is significantly different.In the unstable state,the flame dynamics include three modes—spiral motion mode,flame shedding mode,and axial oscillation mode,whose formation reasons have also been further analyzed in combination with the experimental characteristics.And the fast Fourier transform(FFT)analysis of the time coefficients for the first four POD modes indicates all the dominant frequency is 280 Hz,which means the model combustor is in resonance.In addition,a sensitivity analysis based on the different image resolutions further reveals the robustness of the POD method and its optimization direction.These results emphasize the important influence of the pilot fuel flow rate on the stability of the pilot flame.

Digital image correlation based high-speed crack tip locatingmethod and its application

Based on a digital image correlation（DIC）method with the measurements of a high speed crack＇s displacement and strain fields,a technique to accurately and automatically locate its crack tip has been developed.The crack tip is identified by finding the abrupt jump on the opening（or dislocation）curve of a point on the trace of the crack propagation,while the opening is measured through a virtual extensometer technique and the abrupt jump is identified by finding the peak on the curve.The method was verified using a specially designed experiment and applied to measure the critical crack tip opening angle of a rock sample.Because the involvement of analytical models has been avoided and then the good performance could be ensured for low resolution speckle images,this technique is expected to be very useful in the quantitative study of high speed cracks in experiments using high speed cameras.

Image-Aided Analysis of Ballast Particle Movement Along a High-Speed Railway

As a core infrastructure of high-speed railways,ballast layers constituted by graded crushed stones feature noteworthy particle movement compared with normal railways,which may cause excessive settlement and have detrimental effects on train operation.However,the movement behavior remains ambiguous due to a lack of effective measurement approaches and analytical methods.In this study,an image-aided technique was developed in a full-scale model test using digital cameras and a colorbased identification approach.A total of 1274 surface ballast particles were manually dyed by discernible colors to serve as tracers in the test.The movements of the surface ballast particles were tracked using the varied pixels displaying tracers in the photos that were intermittently taken during the test in the perpendicular direction.The movement behavior of ballast particles under different combinations of train speeds and axle loads was quantitatively evaluated.The obtained results indicated that the surface ballast particle movements were slight,mainly concentrated near sleepers under low-speed train loads and greatly amplified and extended to the whole surface when the train speed reached 360 km.h-1.Additionally,the development of ballast particle displacement statistically resembled its rotation.Track vibration contributed to the movements of ballast particles,which specifically were driven by vertical acceleration near the track center and horizontal acceleration at the track edge.Furthermore,the development trends of ballast particle movements and track settlement under long-term train loading were similar,and both stabilized at nearly the same time.The track performance,including the vibration characteristics,accumulated settlement,and sleeper support stiffness,was determined to be closely related to the direction and distribution of ballast particle flow,which partly deteriorated under high-speed train loads.

Assessing the influence of highway and high-speed railway construction on local climate using Landsat images in karst areas

Large-scale transportation infrastructure construction in ecologically vulnerable areas such as the karst region of Southwest China requires estimation method for better project design.This research was carried out on a four-lane highway(the Guilin-Guiyang highway,G76)and a two-lane highspeed railway(the Guilin-Guiyang high-speed railway,GGHSR)in karst areas in Guizhou and Guangxi provinces.The highway and high-speed railway were constructed in the 2010 s and covered by Landsat images whose multispectral information could be used for research purposes.In this study,the severity of the impact and the CO2 emissions from the G76 and GGHSR construction were evaluated.Landsat images and field meteorological measurements were applied to calculate the surface functional parameters(surface temperature and surface wetness)and heat fluxes(latent,sensible and ground heat flux)before and during the highway and high-speed railway construction;the amount of CO2 emissions during the G76 and GGHSR construction were determined by using budget sheets,which record the detail consumptions of materials and energy.The results showed that the decrease of water evaporation from the highway and high-speed railway construction can reach up to 26.4 m3 and 20.1 m3 per kilometer,which corresponds to an average decrease in the vegetation cooling effect of 18.0 MWh per day per highway kilometer and 13.7 MWh per day per high-speed railway kilometer,respectively.At the meantime,the average CO2 emission densities from the G76 and GGHSR construction can reach up to 24813.7 and 36921.1 t/km,respectively.This study implied that extensive line constructions have a significant impact on the local climate and the energy balance,and it is evident that selecting and planting appropriate plant species can compensate for the adverse effects of line constructions in karst mountain regions.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Dendritic Learning-Incorporated Vision Transformer for Image Recognition

Dear Editor,This letter proposes to integrate dendritic learnable network architecture with Vision Transformer to improve the accuracy of image recognition.In this study,based on the theory of dendritic neurons in neuroscience,we design a network that is more practical for engineering to classify visual features.Based on this,we propose a dendritic learning-incorporated vision Transformer(DVT),which out-performs other state-of-the-art methods on three image recognition benchmarks.

Numerical Study on Reduction in Aerodynamic Drag and Noise of High-Speed Pantograph

Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics theory and the K-FWH acoustic equation,a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs.A component optimization method is proposed as a possible solution to the problemof aerodynamic drag and noise in high-speed pantographs.The results of the study indicate that the panhead,base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs.Therefore,a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise.By optimizing the cross-sectional shape of the strips and insulators,the drag and noise caused by airflow separation and vortex shedding can be reduced.The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest.Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph.In addition,the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise.In contrast to the original pantograph design,the improved pantograph shows a 21.1%reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise.

High-speed penetration of ogive-nose projectiles into thick concrete targets:Tests and a projectile nose evolution model

The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.