基于HSV空间的煤矿井下低光照图像增强方法

针对煤矿井下采集到的图像对比度低、光照不均和细节信息弱等问题,提出一种基于色相-饱和度-明度(Hue-Saturation-Value,HSV)颜色空间的煤矿井下低光照图像增强方法。该方法基于图像的HSV空间,通过对低光照图像的亮度通道V通道的主要结构和边缘细节分别进行对比度增强,这样可以更好地抑制图像细节丢失,同时可以较好地再现原图中的轮廓和纹理细节。首先,将输入的煤矿井下低光照图像转换到HSV空间,利用相对全变分滤波(RTV)与改进的边窗滤波(SWF),分别对提取的V通道图像进行主要结构提取和轮廓边缘保留,对其非线性灰度拉伸后利用主成分分析融合技术(PCA)重构V通道图像,即融合V通道图像的主要结构和精细结构,最后合成图像,完成图像增强。通过实验验证,提出的基于HSV空间的煤矿井下低光照图像增强方法,在色彩和边缘模糊处理等方面表现良好,在煤矿井下工作面等环境中,对图像进行定量和定性实验,结果表明,与6种方法相比,增强图像的对比度、自然度和图像细节方面表现更好。

贵阳市MSM人群中HIV、TP、HSV-2感染的实验结果分析

目的分析贵阳市男男性行为者(MSM)人群中HIV、TP及HSV-2感染的实验检测结果,为该人群的艾滋病防控工作提供参考依据。方法通过同伴推动抽样法招募MSM人员为调查对象,对其血液样本进行HIV、TP及HSV-2检测,并分析实验检测结果。结果577例研究对象中,共发现HIV抗体阳性者、TP特异性抗体阳性者及HSV-2抗体阳性者116例(20.1%)、107例(18.5%)、74例(12.8%);HIV^(+)TP、HIV^(+)HSV-2、TP^(+)HSV-2以及HIV^(+)TP^(+)HSV-2合并感染者24例(4.2%)、8例(1.4%)、11例(1.9%)、20例(3.5%)。实验检测结果分析表明,新发现HIV感染者的WB试验各个反应带型频率不一,带型组合多样化;CD4^(+)T淋巴细胞绝对值计数最小为10个/μL,最大为914个/μL,平均值为(316.2±160.6)个/μL。病毒载量最高值>7 log_(10)/mL,最低值为1.5 log_(10)/mL,平均值为(4.3±0.9)log_(10)/mL。结论贵阳市MSM人群HIV、TP及HSV-2的感染率较高,合并感染情况较为严重,防治形势严峻,应加强本地区MSM人群的HIV、TP及HSV-2防治工作;同时应结合检测结果制定相关的治疗及防治措施,提高感染者的健康水平。

基于Retinex和HSV颜色空间的低照度图像增强算法

针对低照度图像存在的对比度低、颜色失真、细节信息损失、噪声干扰等问题,提出一种基于Retinex的改进算法。首先,将原始图像转换到HSV颜色空间,采用改进算法和Gamma校正对V分量进行亮度调节;其次,基于对比度拉伸算法调整S分量,增强图像饱和度和对比度;最后,将融合后的图像转换回RGB颜色空间,输出图像。在MATLAB平台上选取不同场景低照度图像进行增强处理,采用图像信息熵(IE)、峰值信噪比(PSNR)和结构相似性指数(SSIM)三个客观指标进行均值对比,实验结果表明,新算法计算结果均优于传统的直方图均衡化算法(HE)和多尺度Retinex算法(MSR),有效保持图像边缘信息,提升图像质量。

基于HSV色彩空间及改进二维伽马函数的井下图像增强算法

为解决井下光照条件不足、光照不均引起的视频图像质量低下的问题,构造了一种改进二维伽马函数并以此提出了一种基于HSV色彩空间,作用于图像明度和饱和度分量的井下图像质量增强算法。以综采工作面以及井下泵房处图像为增强对象,将算法与目前主流的图像增强算法进行对比验证。结果表明,在井下环境下该算法对井下低亮度区域有着较好的增强效果,一定程度上满足了井下视频图像质量增强需求。

Vein visualization enhancement by dual-wavelength phase-locked denoising technology

Visual near-infrared imaging equipment has broad applications in various fields such as venipuncture,facial injections,and safety verification due to its noncontact,compact,and portable design.Currently,most studies utilize near-infrared single-wavelength for image acquisition of veins.However,many substances in the skin,including water,protein,and melanin can create significant background noise,which hinders accurate detection.In this paper,we developed a dual-wavelength imaging system with phase-locked denoising technology to acquire vein image.The signals in the effective region are compared by using the absorption valley and peak of hemoglobin at 700nm and 940nm,respectively.The phase-locked denoising algorithm is applied to decrease the noise and interference of complex surroundings from the images.The imaging results of the vein are successfully extracted in complex noise environment.It is demonstrated that the denoising effect on hand veins imaging can be improved with 57.3%by using our dual-wavelength phase-locked denoising technology.Consequently,this work proposes a novel approach for venous imaging with dual-wavelengths and phase-locked denoising algorithm to extract venous imaging results in complex noisy environment better.

Utility of real-time 3D visualization system in the early stage of phacoemulsification training

●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of the first-year postgraduate were included.All the residents were novices to cataract surgery.Real-time cataract surgical observations were performed using a custom-built 3D visualization system.The training lasted 4wk(32h)in all.A modified International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric(ICO-OSCAR)containing 4 specific steps of cataract surgery was applied.The self-assessment(self)and expert-assessment(expert)were performed through the microsurgical attempts in the wet lab for each participant.●RESULTS:Compared with pre-training assessments(self 3.2±0.8,expert 2.5±0.6),the overall mean scores of posttraining(self 5.2±0.4,expert 4.7±0.6)were significantly improved after real-time observation training of 3D visualization system(P<0.05).Scores of 4 surgical items were significantly improved both self and expert assessment after training(P<0.05).●CONCLUSION:The 3D observation training provides novice ophthalmic residents with a better understanding of intraocular microsurgical techniques.It is a useful tool to improve teaching efficiency of surgical education.

Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study

Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Exploring Digital Visualization Methods in Urban Public Building Space Design

Digital technology has driven the innovation of architectural design methods and tools,applying digital techniques to allow greater possibilities for more innovative and scientific design of public building spaces.This article first analyzes the characteristics of digital visualization and its advantages in the design of urban public building spaces,including aspects such as visualizing three-dimensional expression,rational analysis of building space,Virtual Reality Experience,and integration of design and construction processes.Subsequently,by introducing digital design methods such as parametric design,algorithmic generation,nonlinear design,and artificial intelligence-assisted design,it explores the methods and implementation approaches of digital visualization in the design of public building spaces.The aim is to offer insights and references for the deeper integration of digital technology into architectural design practices.

Traditional Chinese Medicine in colorectal cancer treatment:a bibliometrics study and visualization analysis via CiteSpace

Objective:To evaluate the current state of research and areas of interest for traditional Chinese medicine(TCM)in the field of colorectal cancer treatment.Methods:Related papers published between January 1,2012,and November 27,2021,were found using the Web of Science Core Collection Science Citation Index Expanded.Using CiteSpace's network map generation capability,we then determined the top writers,organizations,countries,keywords,co-cited writers,journals,references,and research trends.Results:This investigation yielded a total of 336 relevant papers.China is the most productive country.Shanghai University of Traditional Chinese Medicine is the leading institution.The journal with the most popularity and publishing volume is Evidence-based Complementary and Alternative Medicine.The author with the most citations and centrality is Lin JM.The terms"epithelial-mesenchymal transition,""cell cycle arrest,""apoptosis,"and"autophagy"are highly frequent and have a high betweenness centrality.Conclusion:According to the results,research on natural products,traditional Chinese medicine(TCM)extracts,and the molecular mechanisms of TCM chemical constituents constitutes the primary focus within TCM cancer treatment investigations.In recent years,there has been a surge of interest in exploring the role of gut microbiota in TCM chemical constituents research,particularly in its ability to induce apoptosis and autophagy in tumor cells,thereby suppressing tumor cell proliferation,metastasis,and invasion.However,due to the intricate composition of TCM and existing technical limitations,the underlying principles guiding TCM's efficacy in treating colorectal cancer remain unclear and warrant further investigation.

基于HSV空间的Retinex低照度图像增强算法

针对低照度图像存在亮度较低、照明不均匀、对比度较差、细节信息损失等问题,提出一种基于HSV空间的Retinex低照度图像增强算法.首先将低照度图像转化为HSV空间;然后在HSV空间对亮度通道采用改进的Retinex算法和基于自适应Gamma校正与CLAHE算法联合的亮度增强算法实现对低照度图像的亮度增强;其次,通过双边滤波,利用线性拉伸进行饱和度分量图像增强;最后将增强后的图像重新转换为RGB格式,并利用自动白平衡技术恢复图像物体原本的颜色.实验结果表明,该算法有效改善了低照度图像的质量,达到较好的视觉效果,PSNR、SSIM和信息熵等指标优于其他算法.

基于YOLOv3和HSV颜色空间的绝缘子低/零值故障诊断研究

针对绝缘子在运行过程中长期受恶劣环境的影响而导致其绝缘性能恶化的问题,文中提出了基于YOLOv3和HSV颜色空间的绝缘子低/零值故障诊断技术,以判断出其低/零值故障。首先,采用YOLOv3算法构建瓷绝缘子劣化预测模型,从红外图像中提取瓷绝缘子的最小矩形面积。然后,利用K⁃means算法从矩形区域中分割出绝缘子,以避免背景干扰。在此基础上,利用旋转矩形按长轴方向绘制HSV颜色空间特征归一化值,包括H分量的最大值和最小值、S分量的平均值和V分量的最大值,对绝缘子图像进行校正。最后,结合HSV分量与温升的相关性,推导出瓷绝缘子低/零故障的检测判据,完成对绝缘子运行状态的评价,验证了该方法能够准确区分正常绝缘子和低/零绝缘子。

基于HSV空间融合Retinex算法的全天候运动目标检测

提出了一种在矿山复杂情况下基于HSV色彩空间融合Retinex算法的全天候运动目标检测方法。该方法将运动目标检测转换为运动目标上特定标志物的检测,通过颜色空间转换提取目标原始灰度图像,由灰度分布直方图判断是否进行低照度图像恢复,并在场景中选取特定的标志物,将原图像进行HSV颜色空间变换,选择对应ROI区域,通过阈值获取二值化图像,进行平滑处理后使用Canny算子提取轮廓信息,从而提取标志物的特征参数。应用实例证实,通过该方法能精准获得标志物特征参数,进而解决复杂环境下机械设备间连接件脱落问题,具有一定的实际工程应用价值。

基于HSV颜色空间的建筑工人定位方法研究

建筑工人的安全问题关乎企业利益和工人生命,虽然监控技术迅猛发展,但是建筑工地环境复杂,传统分割方法难以对建筑工地图像中的工人进行较好的分割与识别,该文针对这一问题提出了一种基于色相、饱和度、明度(Hue-Saturation-Value,HSV)颜色空间的建筑工人定位方法。首先,对要处理的图像进行了降噪,利用MATLAB软件的色彩分割工具箱对图像进行分割,获得安全帽区域的二值掩膜;其次,利用生态学处理,获得安全帽所在区域的连通域并进行定位;最后,依据安全帽与工人的比例关系和位置关系,实现了对建筑工人的定位。该文依靠HSV颜色空间实现了对安全帽和建筑工人的定位,解决了建筑工地背景复杂难以分割和定位的问题,对后续研究有一定的参考价值。

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

基于HSV颜色空间的烟叶烘烤阶段判别模型研究

【目的】烟叶烘烤的阶段判别对提升烘烤效率和品质具有重要意义,亟须实现烟叶烘烤阶段自动判别,减少人为影响,提高判别准确率。【方法】首先,使用图像处理技术将图像均衡化,再裁剪并剔除含背景信息多的图块,筛选出有效信息最多的图块;其次,提取烤烟图块的颜色特征,提取每一个图块的HSV三通道值,得到i项通道值;最后,使用基于阈值筛选的方法作为特征处理器,根据不同h、s、v值分类出烟叶烘烤的阶段,并将判断结果与实际情况进行对比验证。【结果】使用图像均衡化处理技术结合基于HSV颜色空间的特征提取算法,进行烟叶烘烤阶段判别,最终整体准确率达到90.64%,并且阶段3和阶段4的准确率达到了100%,效果非常理想。【结论】使用图像处理技术结合图像本身的颜色特征,能有效地判别烤烟的烘烤阶段,对判断烘烤进程、指导烘烤参数调节、提高烘烤品质、减少物料浪费和烘烤成本具有实际意义,有着广阔的应用前景。本研究为后续绕过深度学习等大算力算法但能提高实际应用效果方面的研究提供了方向。

An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

基于彩色偏振图像的HSV空间目标增强方法

针对复杂背景条件下目标难以识别的问题,采用彩色偏振成像技术,提出了一种基于彩色偏振图像的目标增强方法。该方法首先根据分焦平面彩色偏振相机获得的数据得到彩色线偏振度(degree of linear polarization, DoLP)、彩色偏振角(angle of polarization, AoP)和彩色强度(S0)图像;然后利用目标和背景的彩色偏振特性差异大的特点提取DoLP、AoP和S0的视觉显著度,使目标得到初步的增强;随后将3种视觉显著度图像转到HSV空间进行融合,最后转到RGB空间显示。使用对比度和矢量角度距离作为客观评价指标开展实验,多个实验场景数据表明,融合图像的对比度和矢量角度距离分别比融合前图像最高提升了3.971倍和1.711倍。

结合主动光源和改进YOLOv5s模型的夜间柑橘检测方法

【目的】解决夜间环境下遮挡和较小柑橘难以准确识别的问题,实现采摘机器人全天候智能化作业。【方法】提出一种结合主动光源的夜间柑橘识别方法。首先,通过分析主动光源下颜色特征不同的夜间柑橘图像,选择最佳的光源色并进行图像采集。然后,提出一种夜间柑橘检测模型BI-YOLOv5s,该模型采用双向特征金字塔网络(Bi-FPN)进行多尺度交叉连接和加权特征融合,提高对遮挡和较小果实的识别能力;引入Coordinate attention(CA)注意力机制模块,进一步加强对目标位置信息的提取;采用融入Transformer结构的C3TR模块,在减少计算量的同时更好地提取全局信息。【结果】本文提出的BI-YOLOv5s模型在测试集上的精准率、召回率、平均准确率分别为93.4%、92.2%和97.1%,相比YOLOv5s模型分别提升了3.2、1.5和2.3个百分点。在所采用的光源色环境下,模型对夜间柑橘识别的正确率为95.3%,相比白光环境下提高了10.4个百分点。【结论】本文提出的方法对夜间环境下遮挡和小目标柑橘的识别具有较高的准确性,可为夜间果蔬智能化采摘的视觉精准识别提供技术支持。

基于HSV空间的Retinex煤矿井下图像增强算法

对煤矿井下进行实时监控,收取图像信息,并进行安全警报是矿井安全研究的重要内容。然而,受光照与粉尘等因素的影响,使得煤矿井下采集到的图像比较模糊。针对这一影响,提出了一种基于HSV空间的Retinex煤矿井下图像增强算法:将RGB图像映射到HSV空间中,对HSV空间的对应分量分别处理再映射回RGB空间。在实验中,分别对反映矿井整体信息的图像和反映矿井物体局部细节的图像进行增强处理。实验结果表明:相较于传统Retinex算法,经该算法处理过图像的SSIM、PSRN、信息熵指标均有所提高,且更有助于增强图像的边缘细节,这对煤矿井下的安全防范有重要意义。