Frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students

AIM:To investigate the frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students.METHODS:Totally 158 student volunteers underwent routine vision examination in the optometry clinic of Guangxi Medical University.Their data were used to identify the different types of accommodation and nonstrabismic binocular vision dysfunction and to determine their frequency.Correlation analysis and logistic regression were used to examine the factors associated with these abnormalities.RESULTS:The results showed that 36.71%of the subjects had accommodation and non-strabismic binocular vision issues,with 8.86%being attributed to accommodation dysfunction and 27.85%to binocular abnormalities.Convergence insufficiency(CI)was the most common abnormality,accounting for 13.29%.Those with these abnormalities experienced higher levels of eyestrain(χ2=69.518,P<0.001).The linear correlations were observed between the difference of binocular spherical equivalent(SE)and the index of horizontal esotropia at a distance(r=0.231,P=0.004)and the asthenopia survey scale(ASS)score(r=0.346,P<0.001).Furthermore,the right eye's SE was inversely correlated with the convergence of positive and negative fusion images at close range(r=-0.321,P<0.001),the convergence of negative fusion images at close range(r=-0.294,P<0.001),the vergence facility(VF;r=-0.234,P=0.003),and the set of negative fusion images at far range(r=-0.237,P=0.003).Logistic regression analysis indicated that gender,age,and the difference in right and binocular SE did not influence the emergence of these abnormalities.CONCLUSION:Binocular vision abnormalities are more prevalent than accommodation dysfunction,with CI being the most frequent type.Greater binocular refractive disparity leads to more severe eyestrain symptoms.

Measuring Propeller Blade Width Using Binocular Stereo Vision

Propeller blade width measurement has been extensively studied in the past using direct and indirect methods, and it plays a great role in determining the quality of the finished products. It has surveyed that previous techniques are usually time-consuming and erroneous due to a large number of points to be processed in blade width measurement. This paper proposes a new method of measuring blade width using two images acquired from different viewpoints of the same blade. And a new feature points matching approach for propeller blade image is proposed in stereo vision measurement. Based on these, pixel coordinates of contour points of the blade in two images are extracted and converted to real world coordinates by image algorithm and binocular stereo machine vision theory. Then, from the real world coordinates, the blade width at any position can be determined by simple geometrical method.

Measurement method for cluster warhead's dispersion area based on binocular stereo vision technique

Cluster warhead has become the main ammunition of gun,rocket projectile and missile and it has been widely equipped in almost every country.More and more attention is paid to the damage effect of cluster warhead.The size of the dispersion area of cluster warhead is the main standard by which the damage effect of cluster warhead is estimated.The practical method of measuring the dispersion area was developed based on binocular stereo vision measurement theory.The calibration principle of the binocular stereo vision cameras was studied.The matching algorithm that relies on the gradient fields of the neighborhood of a pixel has been used to obtain the spatial information of matched points by acquiring apair of corresponding points in the left and right images of binocular cameras.The 3Dpositions of the flying path of cluster warhead were calculated.The umbrella that is similar to the dispersion track of static explosive cluster warhead was applied in the experiment to get the projection area of the umbrella on the ground.Experiment results verify the feasibility of the proposed method.

Accuracy study of a binocular-stereo-vision-based navigation robot for minimally invasive interventional procedures

BACKGROUND Medical robot is a promising surgical tool,but no specific one has been designed for interventional treatment of chronic pain.We developed a computed tomography-image based navigation robot using a new registration method with binocular vision.This kind of robot is appropriate for minimal invasive interventional procedures and easy to operate.The feasibility,accuracy and stability of this new robot need to be tested.AIM To assess quantitatively the feasibility,accuracy and stability of the binocularstereo-vision-based navigation robot for minimally invasive interventional procedures.METHODS A box model was designed for assessing the accuracy for targets at different distances.Nine(three sets)lead spheres were embedded in the model as puncture goals.The entry-to-target distances were set 50 mm(short-distance),100 mm(medium-distance)and 150 mm(long-distance).Puncture procedure was repeated three times for each goal.The Euclidian error of each puncture was calculated and statistically analyzed.Three head phantoms were used to explore the clinical feasibility and stability.Three independent operators conducted foramen ovale placement on head phantoms(both sides)by freehand or under the guidance of robot(18 punctures with each method).The operation time,adjustment time and one-time success rate were recorded,and the two guidancemethods were compared.RESULTS On the box model,the mean puncture errors of navigation robot were 1.7±0.9 mm for the short-distance target,2.4±1.0 mm for the moderate target and 4.4±1.4 mm for the long-distance target.On the head phantom,no obvious differences in operation time and adjustment time were found among the three performers(P>0.05).The median adjustment time was significantly less under the guidance of the robot than under free hand.The one-time success rate was significantly higher with the robot(P<0.05).There was no obvious difference in operation time between the two methods(P>0.05).CONCLUSION In the laboratory environment,accuracy of binocular-stereo-vision-based navigation robot is acceptable for target at 100 mm depth or less.Compared with freehand,foramen ovale placement accuracy can be improved with robot guidance.

Cast shadow detection for moving objects based on binocular stereo vision

A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera.The points on the plane shared the same position between original image and the transformed image.As a result,the cast shadows can be detected.In order to improve the efficiency of cast shadow detection and decrease computational complexity,the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained.Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations.

Research on DSO vision positioning technology based on binocular stereo panoramic vision system

In the visual positioning of Unmanned Ground Vehicle(UGV),the visual odometer based on direct sparse method(DSO) has the advantages of small amount of calculation,high real-time performance and high robustness,so it is more widely used than the visual odometer based on feature point method.Ordinary vision sensors have a narrower viewing angle than panoramic vision sensors,and there are fewer road signs in a single frame of image,resulting in poor road sign tracking and positioning capabilities,and severely restricting the development of visual odometry.Based on these considerations,this paper proposes a binocular stereo panoramic vision positioning algorithm based on extended DSO,which can solve these problems well.The experimental results show that the binocular stereo panoramic vision positioning algorithm based on the extended DSO can directly obtain the panoramic depth image around the UGV,which greatly improves the accuracy and robustness of the visual positioning compared with other ordinary visual odometers.It will have widely application prospects in the UGV field in the future.

Research on Extraction of Bottom of Shoe Pattern Based on Binocular Stereo Vision

In order to quickly and efficiently get the information of the bottom of the shoe pattern and spraying trajectory, the paper proposes a method based on binocular stereo vision. After acquiring target image, edge detection based on the canny algorithm, the paper begins stereo matching based on area and characteristics of algorithm. To eliminate false matching points, the paper uses the principle of polar geometry in computer vision. For the purpose of gaining the 3D point cloud of spraying curve, the paper adopts the principle of binocular stereo vision 3D measurement, and then carries on cubic spline curve fitting. By HALCON image processing software programming, it proves the feasibility and effectiveness of the method

Experiment Research on Binocular Perceptual Characteristics around Peripheral Vision for Development of Head Mounted Display with Wide View

The head mounted display （HMD） is widely used in virtual reality technology. In common HMD, however, the binocular disparity is set to an equal fixed value in the entire range of view. Such HMD systems have several shortcomings when used for wide views. In this study, in order to realize a natural stereo sensation of HMD with wide view, we measure the characteristics of binocular stereo perception and binocular light perception. Results show that both the stereoacuity and light sensitivity decrease as the retina＇s eccentricity increases from fovea to periphery. However, the decrease of the stereoacuity is more rapid than that of the light sensitivity. These results suggest that the binocular disparity at the peripheral field should be small, otherwise double images would be observed instead of a stereo view. Based on the results we develop a relative binocular stereoacuity model which can be applied for the design of HMD systems with wide view.

RANGE ACQUISITION IN BINOCULAR 3-D COMPUTER VISION USING EDGE-BASED HIERARCHICAL MATCHING

This paper deals with a binocular 3-D computer vision system based on the hierarchicalmatching of edge features, Frei and Chen operator is used to extract the edge. The averagegradients of an image obtained by two isotropic operators are non-equal quantized andthresholded in an angle, Edge features are extracted after passing a preemphasis transferfunction which can equalize, the noise affection. Binary edge images are decomposed into apyramid structure which is stored and searched using llliffe’s location method. Corre-sponding points are used to determine the range data using triangulation based on an improvedTrivedi’s formula. In calibration the authors set the optical axes of the two cameras parallelto simplify the calculation, A 3 rd order Householder transform is used to solve the compati-ble coupled equations.

An Active Stereo Vision System Based on Neural Pathways of Human Binocular Motor System

An active stereo vision system based on a model of neural pathways of human binocular motor system is proposed. With this model, it is guaranteed that the two cameras of the active stereo vision system can keep their lines of sight fixed on the same target object during smooth pursuit. This feature is very important for active stereo vision systems, since not only 3D reconstruction needs the two cameras have an overlapping field of vision, but also it can facilitate the 3D reconstruction algorithm. To evaluate the effectiveness of the proposed method, some software simulations are done to demonstrate the same target tracking characteristic in a virtual environment apt to mistracking easily. Here, mistracking means two eyes track two different objects separately. Then the proposed method is implemented in our active stereo vision system to perform real tracking task in a laboratory scene where several persons walk self-determining. Before the proposed model is implemented in the system, mistracking occurred frequently. After it is enabled, mistracking never occurred. The result shows that the vision system based on neural pathways of human binocular motor system can reliably avoid mistracking.

Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

Current research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional(3D)objects.The classical Zhang’calibration is hardly to calculate all errors caused by perspective distortion and lens distortion.Also,the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces.In this paper,a preset coordinate system was utilized for camera calibration instead of Zhang’calibration.The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding.Combining and improving the algorithms of speeded up robust features,binary robust invariant scalable keypoints,and KAZE,the feature information of points(i.e.,RGB values,pixel coordinates)was extracted as the feature vector of the welding pool surface.Based on the characteristics of the welding images,a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms.The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface.The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results.This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.

Vision based terrain reconstruction for planet rover using a special binocular bundle adjustment

This paper presents a pure vision based technique for 3D reconstruction of planet terrain. The reconstruction accuracy depends ultimately on an optimization technique known as 'bundle adjustment'. In vision techniques, the translation is only known up to a scale factor, and a single scale factor is assumed for the whole sequence of images if only one camera is used. If an extra camera is available, stereo vision based reconstruction can be obtained by binocular views. If the baseline of the stereo setup is known, the scale factor problem is solved. We found that direct application of classical bundle adjustment on the constraints inherent between the binocular views has not been tested. Our method incorporated this constraint into the conventional bundle adjustment method. This special binocular bundle adjustment has been performed on image sequences similar to planet terrain circumstances. Experimental results show that our special method enhances not only the localization accuracy, but also the terrain mapping quality.

基于Vision Transformer的虹膜——人脸多特征融合识别研究

为了提高生物特征识别系统的准确性和鲁棒性,本文研究基于计算机视觉的虹膜—人脸多特征融合识别方法。本文对面部图像中虹膜区域进行提取以及预处理,采用对比度增强和归一化操作,加强了特征提取的一致性,提升了图像质量。为了获取丰富的深度特征,本文使用Vision Transformer模型对预处理后的虹膜和面部图像进行特征提取。利用多头注意力机制将虹膜和面部的多模态特征信息进行融合,再利用全连接层进行分类识别。试验结果表明,该方法识别性能优秀,识别准确性显著提升。

基于Vision Transformer的小麦病害图像识别算法

小麦白粉病、赤霉病和锈病是危害小麦产量的三大病害。为提高小麦病害图像的识别准确率,构建一种基于Vision Transformer的小麦病害图像识别算法。首先,通过田间拍摄的方式收集包含小麦白粉病、赤霉病和锈病3种病害在内的小麦病害图像,并对原始图像进行预处理,建立小麦病害图像识别数据集;然后,基于改进的Vision Transformer构建小麦病害图像识别算法,分析不同迁移学习方式和数据增强对模型识别效果的影响。试验可知,全参数迁移学习和数据增强能明显提高Vision Transformer模型的收敛速度和识别精度。最后,在相同时间条件下,对比Vision Transformer、AlexNet和VGG16算法在相同数据集上的表现。试验结果表明,Vision Transformer模型对3种小麦病害图像的平均识别准确率为96.81%,相较于AlexNet和VGG16模型识别准确率分别提高6.68%和4.94%。

基于Vision Transformer与迁移学习的裤装廓形识别与分类

针对裤装廓形识别与分类模型的分类不准确问题,文章采用带有自注意力机制的Vision Transformer模型实现裤装廓形图像的分类,对于图片背景等无关信息对廓形识别的干扰,添加自注意力机制,增强有用特征通道。为防止因裤型样本数据集较少产生过拟合问题,可通过迁移学习方法对阔腿裤、喇叭裤、紧身裤、哈伦裤4种裤装廓形进行训练和验证,将改进的Vision Transformer模型与传统CNN模型进行对比实验,验证模型效果。实验结果表明:使用Vision Transformer模型在4种裤装廓形分类上的分类准确率达到97.72%,与ResNet-50和MobileNetV2模型相比均有提升,可为服装廓形的图像分类识别提供有力支撑,在实际服装领域中有较高的使用价值。

细粒度图像分类上Vision Transformer的发展综述

细粒度图像分类(fine-grained image classification,FGIC)一直是计算机视觉领域中的重要问题。与传统图像分类任务相比,FGIC的挑战在于类间对象极其相似,使任务难度进一步增加。随着深度学习的发展,Vision Transformer(ViT)模型在视觉领域掀起热潮,并被引入到FGIC任务中。介绍了FGIC任务所面临的挑战,分析了ViT模型及其特性。主要根据模型结构全面综述了基于ViT的FGIC算法,包括特征提取、特征关系构建、特征注意和特征增强四方面内容,对每种算法进行了总结,并分析了它们的优缺点。通过对不同ViT模型在相同公用数据集上进行模型性能比较,以验证它们在FGIC任务上的有效性。最后指出了目前研究的不足,并提出未来研究方向,以进一步探索ViT在FGIC中的潜力。

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

基于Vision Transformer的阿尔茨海默病分类研究

为了有效地提升对阿尔茨海默病(AD)的磁共振成像(MRI)图像分类准确率,提出一种LC(Layer-Cut)-ViT方法。该方法通过引入Vision Transformer(ViT)的自注意力机制对MRI图像进行层切分,使模型能更好地理解图像的全局信息,同时突出切片间的特征关系。此外,通过配准、颅骨分离算法提取MRI图像的脑部组织部分,进一步提升模型的性能。实验结果显示,所提方法对阿尔茨海默病的MRI图像具有较好的分类能力。

基于Vision Transformer和迁移学习的家庭领域哭声识别

针对SVM等传统机器学习算法准确率低和当前使用CNN处理家庭领域哭声识别在不同婴儿间出现泛化能力差的问题,提出了一种基于Vision Transformer和迁移学习的婴儿哭声音频分类算法。首先,为实现数据集样本的扩增,采用了包括梅尔频谱转换和数据增强的数据预处理技术,进而达到了增强模型鲁棒性的目的。而后,在微调后的Vision Transformer模型上进行迁移学习训练,同时,训练过程中利用了LookAhead优化器来不断调整模型参数以避免过拟合,最终实验实现了对婴儿哭声音频的自动分类。实验结果表明,本实验模型相比其他深度学习模型具有更高的精确率和更快的收敛速度,同时还能有效地学习到婴儿哭声中更具区分性的特征。可以在新生儿监护、听力筛查和异常检测等领域中发挥重要作用。

基于Vision Transformer和迁移学习的垃圾图像分类研究

为解决垃圾图像分类中分类准确率低及小样本类别性能差的问题,以生活垃圾图像为研究对象,以正确识别生活垃圾类别为研究目标,利用Vision Transformer模型为分类网络架构,使用迁移学习机制实现该模型在华为云垃圾分类数据集上的训练及分类推理。实验结果表明,基于注意力机制的分类模型相较于基于卷积结构的ResNet、DenseNet分类模型具有更高的分类准确率,可达96%,同时测试集的混淆矩阵表明Vision Transformer分类模型在样本不均衡数据集中对于小样本类别也具有较高的准确率,具有实际部署、推理的应用价值。