机器视觉论文
计算机视觉技术方面的论文（2）
计算机视觉技术方面的论文(2)计算机视觉技术方面的论文篇二《基于OPENCV的计算机视觉技术研究》【摘要】伴随着科技不断发展，基于OPENCV的计算机视觉技术应运而生，该技术的出现受到了社会的广泛关注。
本文将对计算机视觉技术应用原理进行分析，论述基于OPENCV的运动物体检测。
并且从三个角度分析基于OPENCV的图像预处理技术，为计算机视觉技术创新提供依据。
【关键词】OPENCV;计算机视觉技术;研究计算机视觉技术能够有效的实现人眼的分类、分割、跟踪以及判别等，在先进的技术下，在视觉系统中能够创建出3D等图像元素数据，并且根据系统需求获取信息。
基于OPENCV的计算机视觉技术研究比较晚，在诸多方面还处于探索阶段，在该技术研发环节中还存在着很多问题。
一、计算机视觉技术应用原理基于OPENCV的计算机视觉技术，应用于视频中运动物体检测时，主要分为宏观和微观两种检测方式。
其中宏观检测方式是指，以视频中的某一个画面为研究对象，研究内容比较整体。
而微观检测方式在整幅图像的基础上，截取一小部分，根据实际需求在一小部分内部进行检测。
在进行计算机视觉技术研究当中分为两个环节，第一环节，图像数据信息的采集，第二环节，对于图像数据信息预处理。
例如，当在宏观的图像数据分析下，只需要对图像进行整体分析就可以，而在微观的检测技术下，需要根据技术需求，对图像进行科学分割。
二、基于OPENCV的运动物体检测在对与动物体进行检测的环节中，在OPENCV技术基础上，需要对视频中运动的物体进行特征性的捕捉。
一般正在运动的物体其轮廓、颜色以及性状等比较特殊，在特征捕捉中比较便捷。
通过宏观的物体运动捕捉，将所在运动的物体与运动背景相互分离，以便于对运动物体的数据信息进行研究。
基于基于OPENCV的运动物体检测主要分为三个步骤：第一，视频流的捕捉;在进行图与像分离的过程中，需要对视频流进行科学的捕捉，保障所得的图像数据信息真实、清晰而完整。
计算机视觉论文
计算机视觉论文1000字计算机视觉是指计算机利用图像处理、模式识别、计算几何、人工智能等技术实现对图像的理解与分析，从而使计算机从图片、视频等视觉信息中获取更丰富的信息，并利用这些信息完成人们所需要的各种功能和任务。
下面介绍几篇比较经典的计算机视觉论文。
1. R-CNN: Object Detection via Region-based Convolutional Networks这篇论文由Ross Girshick等人在2014年提出，是深度学习在目标检测领域的开山之作。
该方法将传统的滑动窗口式检测方式替换成针对提取候选区域的局部卷积神经网络（region-based convolutional network, R-CNN）。
此方法首先提取一系列候选框（region proposals），然后将这些框区域输入到卷积神经网络中进行分类和回归。
该模型最终能够实现高准确率的目标检测，同时也大大缩短了计算时间。
2. Deep Residual Learning for Image Recognition这篇论文由Kaiming He等人于2016年提出。
该论文主要研究了深度网络的深度和精度之间的矛盾，并提出了残差学习的思路。
残差学习通过增加跨层连接，将网络的前后输出进行直接相加，从而使得网络学习到不同的特征时不会失去过多原有的信息。
这种方法的应用不仅能够提高深度网络的精度，还能够帮助深度网络降低梯度消失等问题。
3. Generative Adversarial Networks该论文由Ian Goodfellow等人于2014年提出。
这是一种生成式模型，通过在训练过程中，同时训练一个生成器网络并一个判别器网络，从而实现高效的数据生成。
该方法的创新之处在于将生成式模型的随机噪声与判别式模型的决策结合起来，通过互相博弈的方式逐渐提升网络的表现。
该方法不仅能够生成高质量、多样性的样本数据，也可以在图像修复、语音识别等任务中得到广泛应用。
机器视觉毕业设计
机器视觉是计算机视觉的一个分支，涉及计算机系统对图像或视频的处理、分析和理解。
机器视觉在许多领域中都有广泛的应用，例如图像识别、目标追踪、人脸识别等。
以下是一些机器视觉毕业设计的主题和方向，供参考：1. 基于深度学习的图像分类系统：-设计一个基于深度学习的图像分类系统，使用卷积神经网络（CNN）等深度学习模型，对图像进行准确分类。
2. 实时目标检测与追踪系统：-开发一个实时的目标检测与追踪系统，利用目标检测算法如YOLO（You Only Look Once）或Faster R-CNN，并实现目标的实时追踪。
3. 人脸识别系统及性能优化：-设计一个人脸识别系统，可以用于人脸解锁、身份验证等场景，并对系统性能进行优化，提高识别速度和准确性。
4. 三维重建与虚拟现实（VR）应用：-利用机器视觉技术进行三维场景重建，实现虚拟现实应用，例如虚拟博物馆、虚拟旅游等。
5. 医学图像分析与诊断辅助：-开发医学图像分析系统，通过机器学习算法对医学图像进行分析，提供医生的诊断辅助。
6. 无人驾驶车辆的视觉感知：-研究无人驾驶车辆的机器视觉感知系统，包括交通标志识别、障碍物检测等，以提高自动驾驶的安全性。
7. 深度学习在农业领域的应用：-基于深度学习技术，开发农业领域的图像处理系统，用于农作物病害检测、生长监测等。
8. 图像处理与艺术创作：-利用机器视觉技术进行艺术创作，例如生成对抗网络（GAN）生成艺术作品，或者基于图像的创意设计。
9. 基于视觉SLAM的室内导航系统：-研究并设计基于视觉SLAM（Simultaneous Localization and Mapping）的室内导航系统，用于实现室内环境中的定位和导航。
10. 智能监控系统：-设计智能监控系统，包括行为识别、异常检测等功能，用于提升监控系统的智能化水平。
这些主题都涉及到机器视觉领域的不同方面，具体选择可根据个人兴趣、专业方向以及导师的建议来确定。
《2024年基于机器视觉的服务机器人智能抓取研究》范文
《基于机器视觉的服务机器人智能抓取研究》篇一一、引言随着科技的飞速发展，服务机器人逐渐成为人们日常生活和工作中的重要组成部分。
其中，智能抓取技术作为服务机器人的关键技术之一，具有广阔的应用前景。
然而，如何提高机器人在抓取过程中的准确性、灵活性和效率成为了研究的重点。
本文旨在探讨基于机器视觉的服务机器人智能抓取技术的研究现状和未来发展。
二、研究背景与意义在传统的手工操作中，人们依赖视觉信息来识别和抓取物体。
而基于机器视觉的服务机器人智能抓取技术，通过模拟人类的视觉系统，使机器人能够自主地识别、定位和抓取物体。
这种技术不仅提高了生产效率，降低了人力成本，还为服务机器人提供了更广泛的应用场景。
例如，在医疗、农业、物流等领域，智能抓取技术都有着巨大的应用潜力。
三、机器视觉在智能抓取中的应用机器视觉在智能抓取中发挥着至关重要的作用。
首先，通过图像处理和模式识别技术，机器人能够准确地识别和定位目标物体。
其次，利用深度学习和人工智能算法，机器人可以学习和优化抓取策略，提高抓取的准确性和效率。
此外，机器视觉还可以为机器人提供丰富的环境信息，帮助其更好地适应不同的抓取任务。
四、智能抓取技术研究现状目前，基于机器视觉的服务机器人智能抓取技术已取得了一定的研究成果。
例如，一些机器人已经能够通过图像识别和定位技术，实现自动抓取和装配等任务。
然而，在实际应用中，仍存在一些挑战和问题。
例如，当物体摆放位置不规律、表面反光或存在遮挡时，机器人的抓取准确性会受到影响。
此外，对于复杂的抓取任务，如多物体同时抓取、柔性物体抓取等，仍需要进一步研究和优化。
五、智能抓取技术的研究方法与实现针对上述挑战和问题，本文提出了一种基于深度学习的智能抓取方法。
首先，我们利用深度相机获取物体的三维信息，并通过图像处理技术提取出物体的特征。
然后，利用深度学习算法训练一个抓取预测模型，该模型能够根据物体的特征和机器人的当前状态，预测最佳的抓取姿势和位置。
机器视觉技术在低光照环境下的目标检测与识别研究
机器视觉技术在低光照环境下的目标检测与识别研究近年来，机器视觉技术在许多领域取得了巨大的进展，其中之一就是目标检测与识别。
随着技术的不断发展和应用场景的扩大，人们对机器视觉技术在低光照环境下目标检测与识别的需求也越来越迫切。
本文将对机器视觉技术在低光照环境下的目标检测与识别进行研究。
低光照环境下的目标检测与识别是一个具有挑战性的任务。
由于光线较暗，图像中的目标往往难以清晰地显示出来，造成目标的边缘模糊、颜色信息丢失等问题。
针对这些问题，研究者们提出了一系列的解决方案。
首先，图像增强是低光照环境下目标检测与识别的关键技术之一。
通过对图像进行增强，可以提高图像的对比度、增强图像的边缘等，从而增强目标的可见性。
常见的图像增强方法包括直方图均衡化、自适应直方图均衡化、Retinex算法等。
这些方法可以有效地提高图像的质量，使得目标更加清晰可见。
其次，特征提取也是低光照环境下目标检测与识别的关键一步。
由于光线较暗，图像中的细节信息难以获取，传统的特征提取方法可能失效。
因此，研究者们提出了一系列针对低光照环境的特征提取算法。
例如，基于深度学习的特征提取算法可以学习到更加鲁棒的特征表示，从而提高目标检测和识别的准确性。
此外，也有研究者提出了一些基于纹理特征和边缘特征的方法，通过充分利用目标在低光照下的纹理和边缘信息来进行目标检测与识别。
目标检测与识别的另一个挑战是实时性。
在低光照环境下，由于图像质量较低，传统的目标检测与识别算法往往需要耗费更多的时间来进行计算，从而导致实时性较差。
为了解决这个问题，研究者们提出了一系列的加速算法。
例如，通过降低图像的分辨率、减少特征的维度、优化算法的计算过程等方式，可以大幅提升算法的计算速度，进而提高实时性。
此外，深度学习技术在低光照环境下的目标检测与识别中也得到了广泛的应用。
深度学习模型具有强大的学习和表达能力，可以从大量的数据中学习到目标的特征表示，从而提高目标检测和识别的准确性。
机器视觉模型分析论文
______________________________________________________________________________________________________________________________Report Information from ProQuestMarch 13 2014 03:12_______________________________________________________________目录1. Model analysis and experimental technique on computing accuracy of seam spatial position information based on stereo vision for welding robot (1)第 1 个文档，共 1 个Model analysis and experimental technique on computing accuracy of seam spatial position information based on stereo vision for welding robotProQuest 文档链接摘要: Purpose - Stereo vision technique simulates the function of the human eyes to observe the world, which can be used to compute the spatial information of weld seam in the robot welding field. It is a typical kind of application to fix two cameras on the end effector of robot when stereo vision is used in intelligent robot welding. In order to analyse the effect of vision system configuration on vision computing, an accuracy analysis model of vision computing is constructed, which is a good guide for the construction and application of stereo vision system in welding robot field. Design/methodology/approach - A typical stereo vision system fixed on welding robot is designed and constructed to compute the position information of spatial seam. A simplified error analysis model of the two arbitrary putting cameras is built to analyze the effect of sensors' structural parameter on vision computing accuracy. The methodology of model analysis and experimental verification are used in the research. And experiments related with image extraction, robot movement accuracy is also designed to analyze the effect of equipment accuracy and related processed procedure in vision technology. Findings - Effect of repeatability positioning accuracy and TCP calibration error of welding robot for visual computing are also analyzed and tested. The results show that effect of the repeatability on computing accuracy is not bigger than 0.3 mm. However, TCP affected the computing accuracy greatly, when the calibrated error of TCP is bigger than 0.5, the re-calibration is very necessary. The accuracy analysis and experimental technique in this paper can guide the research of three-dimensional information computing by stereo vision and improve the computed accuracy. Originality/value - The accuracy of seam position information is affected by many interactional factors, the systematic experiments and a simplified error analysis model are designed and established, the main factors such as the sensor's configurable parameters, the accuracy of arc welding robot and the accuracy of image recognition, are included in the model and experiments. The model and experimental method are significant for design of visual sensor and improvement of computing accuracy.链接: CALIS e得文献获取, PQDT全文库链接,UNICAT联合目录(刊名),公共查询系统全文文献: 1 IntroductionComputer vision is widely used in modern industrial production. Stereo vision, as a well-known method of computer vision technology, uses a vision system with two or more cameras to gain geometry information of tested object from the principle of visual disparity computation. Almost all high-end robotic systems are now equipped with pairs of cameras to provide depth perception. While humans tend to take depth perception for granted, judging depth is difficult for computers, and remains a subject of ongoing research. Stereo vision technology is used in the welding field to compute the spatial (or 3D) information of the weld seam by simulating the welder's eyes. This method has been used frequently on arc welding robots and made them more intelligent ([7] Tarn et al. , 2007; [1] C and S, 2009; [4] Shi et al. , 2010). A "hand-eyes" system is built up by fixing two cameras on the end effector of the welding robot to compute the weld seam's 3D coordinates in robot coordinate system through image recognition, camera calibration, stereo matching and 3D reconstruction ([5] Jain et al. , 2003). This technology has been well studied. Significant research has been conducted on vision system computing techniques for a variety of tasks such as guiding of position ([3] Chen et al. , 2006; [8] Chen and Chen, 2010), seam tracking ([10] Zhang et al. , 2009), pose detection ([6] Park et al. , 2009), and trajectory reconstruction ([2] Chen et al. , 2005; [1] Cojanu and Marra, 2009). However, most studies have not addressedaccuracy analysis and effect-factors for the use of stereo vision technology robotic arc welding. Many factors, such as the sensor's configuration parameters, the accuracy of arc welding robot and the accuracy of image recognition, may affect the final calculated results. In this paper, a mathematical model of stereo vision is established to research the relationship between sensor's configurable parameters and the tested point of weld seamand an experimental method is designed to evaluate the model. The effect of the factors on computing 3D information about the weld seam is studied. The model and experimental method provide great insight into the design of weld seam vision sensor and into the improvement of computing accuracy.2 Principle of 3D information computing with stereo visionStereo vision perceives 3D information by simulating the function of the human eyes, i.e. it perceives depth by visual disparity computation. The most commonly used method is to capture images of the same target from different positions with two cameras and then compute distance using the principle of visual disparity, based on triangulation, as shown in Figure 1 [Figure omitted. See Article Image.].In Figure 1 [Figure omitted. See Article Image.], Cl and Cr are the locations of the left and right cameras, respectively, f is the focal length on both cameras. The origin of coordinate system is assumed to be coincident with the center of the left camera lens. The distance between the cameras is b and is called the baselinedistance. P(x,y,z) is a random point in space. Its projective points in left and right visual plane are pl and pr,respectively. X prime is the distance of the projection of P in the x-direction in each camera visual plane. Therelationship of similar triangles PMCl and plLClcan be expressed as follows: Equation 1 [Figure omitted. SeeArticle Image.] Similarly, the relationship of similar triangles PNCr and plRCrcan be expressed as follows:Equation 2 [Figure omitted. See Article Image.] After combining equations (1) and (2) above, we get: Equation 3[Figure omitted. See Article Image.] where x [variant prime]l -x [variant prime]ris the visual disparity.Thus, depth interpretation of the scene points can be realized by computing visual disparity which results in the (x, y, z) coordinates of the scene point P in the world coordinate system. From formula (3), to increase the baseline distance b is an effective way to increase depth computing accuracy of scene point if the camera parameters are fixed. However, the common field-of-view of the two cameras will decrease with the increasing of b and P becomes invisible if b increases beyond a certain value. Increasing visual disparity enlarges the search field of matching points and increases the mismatch probability during stereo matching. The computing accuracy also has close relationships with configurable camera parameters. As to arc welding robot, the 3D coordinates of the visualize weld seam must be transformed to the robot coordinate system in order to control the robot's movement properly. Thus, the computing accuracy is also influenced by accuracy of the robot itself and calibration of TCP. Analysis of the effect of these factors will be given in this paper. Detailed 3D vision computing procedures of the weld seam can be found elsewhere ([2] Chen et al. , 2005; [8] Chen and Chen, 2010).3 Error analysis and experiments3.1 Effect of the accuracy of robot positionUsing two cameras mounted on the end-effector, we can calculate the 3D coordinates of the weld seam in the camera coordinate system. However, the control command must be expressed only in the coordinate system of welding robot or the world. Thus, the transformation from the camera coordinate system to robot coordinate system is necessary in the robotic welding. The relative position and orientation between welding robot end effector and the cameras, i.e. "hand-eyes" relationship, must be obtained in this process. The pose of robot has an important effect on accuracy during the camera/robot calibration and thus, the accuracy of the robot directly determines the final calculated accuracy.Two types of robots were compared in this experiment, a MotoMan UP6 with a repeatability accuracy of ±0.02 mm and a MotoMan SR6 with a repeatability accuracy of ±0.08 mm. Theoretically, the end of the tool (here, the end of the welding torch) is the control point after TCP calibration. Position of TCP is fixed while the robot rotates about any axis X, Y and Z, as shown in Figure 2(a) [Figure omitted. See Article Image.]. But in practicalapplication, the actual TCP will deflect to some extent because of the accuracy of the robot itself and the error of TCP calibration, as shown in Figure 2(b) [Figure omitted. See Article Image.]. After calibration, the error of the UP6 robot is 0.2-0.5 mm and the error of SR6 robot is assumed to be 3-5 mm.3.1.1 Robot static repeatability errorThe effect of robot repeatability error on the accuracy of 3D scene information computing is considered first. Cameras move with the robot to a certain pose to capture image pairs of a standard chessboard. Later, the robot moves the cameras to a random position and then back to the place (same robot pose) where the images were captured before, and additional image pairs are captured again. The process is repeated to collect 20 photos of the chessboard. As shown in Figure 3 [Figure omitted. See Article Image.], the size of each standard chessboard square is 27×27 mm. A total of three or four squares were selected for measurement in this study. The effect of robot positional repeatability error on vision system position results can be confirmed by comparing of the known standard and measured chessboard square size.The data from the repeated measurements of three squares (81 mm) and four squares (108 mm) lattices in two poses are shown in Table I [Figure omitted. See Article Image.]. The maximum distance error between calculated values and standard values is 0.55 mm. This error includes the computing error of the vision system and the construction error of the standard chessboard. Computational stability must be considered when weighing the effect of robot repeatability positioning accuracy on vision computing. Standard deviation (SD) is a good indicator to appraise the data dispersion. Here, data from 20 repeated measurements of three different chessboards was used to compute the overall SD and was found to be ±0.30 mm. This result shows the effect of static robot repeatability accuracy on the results of vision position computing with the error computed in camera coordinate system. The error of different robot poses and calibration error between the robot and camera coordinate systems are not included.3.1.2 Influence of different robot poses and TCP miscalibrationAnother experiment is designed for further study of effect of the robot moved to different poses and calibration accuracy of TCP on image and final positioning accuracy. The standard chessboard is fixed on workbench and the robot TCP is calibrated. The robot pose is adjusted to take a series of image pairs of the chessboard. Figure 4 [Figure omitted. See Article Image.] shows a group of image series of 18 image pairs. The coordinate of a selected chessboard corner point in the robot coordinate system is to be calculated by visual computing. Here, we chose the top left corner of the chess board whose coordinates, found by robot teaching playback, was found to be (902.653, -103.251, -6.675). The coordinates of the selected fixed point should be the same in all images according to theory. But factors like moving the robot to different poses can lead to a deviation. Here we assume that the results of teaching and playback allow the robot to return to the same point. Figure 5 [Figure omitted. See Article Image.] shows calculated 3D coordinates of the selected point on the chessboard in the robot coordinate system. The standard error which includes the errors of images and robot movement is less than 0.54 mm when the calibration error of TCP is constant (0.3 mm).The effect of TCP calibration on vision computing can be tested by the similar method. First, we calibrate the TCP for several times each with a different calibration error. Then the vision computing is carried out according to the method shown in Figures 4 and 5 [Figure omitted. See Article Image.]. In total, 18 calculated values can be obtained by changing the robot pose 18 times for each calibrated TCP. The value of TCP calibration error cannot be controlled artificially. Thus, we change the calibration error of TCP every time purposely, but we cannot control the value of calibrated error of TCP. Five different TCP errors were obtained in the experiment. Table II [Figure omitted. See Article Image.] shows the maximum absolute value of TCP error and SD of X, Y, Z distances by comparing calculated 3D coordinates with measured coordinates from teaching and playback. We can see that both the SD and maximum error increase non-linearly with increasing TCP error. The calculated error is over 1 cm when maximum TCP error is 1.8 mm. The error is more than 10 cm when maximum TCP error reaches 3.7 mm, well beyond requirement for practical production. Practically, the TCP must be re-calibrated when its error is greater than 0.5 mm.The two experiments above explain the effect of robot accuracy on vision computing. Today, mature industrial robots have a high accuracy of repeatability positioning. While the repeatability positioning error of the robots in this paper can be controlled below 0.5 mm, the positioning error of most modern robots is between 0.2 and 0.5 mm. High repeatability positioning accuracy minimizes leads to a low effect on vision computing. However, the TCP should be calibrated accurately because it has a greater effect on vision computing. In order to obtain accurate information, re-calibration is mandatory when the TCP error is greater than 0.5 mm.3.2 Vision computing error analysis modelConfiguration of vision system has an obvious effect on vision computing accuracy. Factors like hardware and configuration of cameras can influence the computing accuracy to a certain degree. First of all, the work-piece must be put in the effective field of view (FOV) of the two cameras at the same time. The field outside of the common FOV is called "blind area". Even though the work-piece is in the common effective FOV, the detection accuracy is not same when the work piece is in different positions. Thus, it is necessary to analyze the effect of work-piece pose in the effective FOV and its relative position to the camera sensors on vision computing accuracy.A simplified error analysis model of the two arbitrarily placed cameras is built to analyze the effect of the camera sensors' structural parameters on vision computing accuracy, as shown in Figure 6 [Figure omitted. See Article Image.]. In this paper, the left camera is chose to be a reference, and the right camera to be the conjugate one. Perspective center Olof left camera CCD is the coordinate system origin, and the direction of the base line is x-axis. The axis perpendicular to x on image plane of CCD is the y-axis. The z-axis or depth direction is confirmed by right-hand rule. The effective FOV of the stereo sensor on the height direction is the same for that of a single camera. Only the FOV on the direction of depth and width in xOz plane will be discussed. The FOV is determined by b, α, ßand structural parameters of the camera's depth of field. The distance between the left and right camera CCD sensors perspective centers is b and the view field angle of the cameras on the xOz plane is 2[straight theta]. The angle of the two cameras' optic axes is 2ß. Connecting with the crossover point of cameraoptic axis and its projection on Ol Or, we can get the angles of the left and right cameras' optic axes and theconnected line, which are ßl and ßr, respectively, and 2ß=ßl+ßr. The cameras are arbitrarily placedsymmetrically in a general way. As shown in Figure 6 [Figure omitted. See Article Image.], the field above MSN along the forward direction of z-axis is the effective view field of the stereo vision system. w is the width of effective view field in the horizontal direction. P(x, y, z) is assumed to be a point in this field and its projectionson left and right images are pl (ul, vl) and pr(ur, vr), respectively. αland αrare the flare angles of point Prelative to the perspective centers of the left and right cameras. Features of the two cameras are assumed to be exactly the same to simplify the analysis. The two camera focal lengths can be approximately represented as fl =fr=f . The two cameras should be placed as symmetrical as possible.According to the trigonometry, x, y, z can be expressed as follows: Equation 4 [Figure omitted. See Article Image.]Equation 5 [Figure omitted. See Article Image.]Equation 6 [Figure omitted. See Article Image.]3.3 Effect and experiment of vision system configuration3D coordinates of points within the stereo vision system have known relationships with the camera sensor's configurable parameters b, f, α, ßin addition to their pixel coordinates on image plane. The computing of 3D coordinates can be summed up as the following vector function: Equation 7 [Figure omitted. See Article Image.] All of the parameters in the function above are not independent from each other. For example, correlations exist not only between f and [straight theta], but also α, ßand u, v. According to a theory of error analysis ([9] Yetai, 2000), a comprehensive error can be represented by measurement error on directions of x, y, z as follows: Equation 8 [Figure omitted. See Article Image.] Error transfer function [varphi]iof all the factors is defined asfollows: Equation 9 [Figure omitted. See Article Image.] where M takes the value of x, y, z which presents the factors u l ,v l ,u r ,v r ,f l ,f r ,αl ,ßl ,αr ,ßr ,[straight theta] , respectively; δi is the ith error component to Δ.Therefore, influencing factors on the comprehensive error of P are the follows: sensor's configurableparameters b, f, ßl ,ßr and its calibration error; projection of P on image and image quantization error andcoordinates extraction error. The position variation and the extraction error of P lead to an alteration of αl and αr , which are correlation parameters. For a certain system, the variable elements are the position of P and its deviation. The physical world is continuous but the image on CCD is discrete and scattered. Thus, a discrete error and extraction error of feature points exists. Projections of P on image planes should be p l and p r , as shown in Figure 6 [Figure omitted. See Article Image.]. But they become p l [variant prime] and p r [variant prime]in practice after feature extraction. Deviations will also occur during the revision and matching of stereo image pairs. We call these deviations image errors. The left and right camera image errors in the direction of u and v are assumed to be Δ[straight epsilon] and Δω and donot change with image position. The derivatives of x, y, z to u, v are computed, respectively. The partial derivatives of z in the left and right image are assumed to be Δ z -u l =(∂z /∂u l ), Δx -u 1 =(∂x /∂u 1 ). In the same way, Δx -u 1 , Δx -ur , Δy -ul , Δy -ur , Δy -vl , Δy -vr can be obtained. The effect ofthese factors on computing can be represented as follows: Equation 10 [Figure omitted. See Article Image.]Equation 11 [Figure omitted. See Article Image.] Equation 12 [Figure omitted. See Article Image.] From the derivation of Δ x ,Δy ,Δz , we can see the comprehensive error Δ is a complicated function of position of P(x, y,z), b ,f ,ßl ,ßr . The comprehensive error Δ is hard to describe accurately, but a relationship of inverseproportionality between Δ and b, f can be qualitatively obtained. Error is related to coordinates of tested point.For example, in Figure 6 [Figure omitted. See Article Image.], Q is a point above P and its corresponding angles αl ,αr are smaller than that of P. And the corresponding cos(αl ),cos(αr ) becomes greater, which results in abigger error. For a specific analysis of the effect of image capturing position (or the position of workpiece space points) on vision computing, we assume the configuration of vision system is fixed. That means b and f have fixed values. To simplify the analysis, we assume ßl =ßr =ß , and image error the u, v-directions are the same,i.e. Δ[straight epsilon]=Δω. As a result, formula (5) can be simplified to a large extent and can be represented as follows: Equation 13 [Figure omitted. See Article Image.] Here, the vision system has been calibrated by Zhang method ([11] Zhang, 2000) with f x =588.4748 pixel, f y =586.6578 pixel, b=229.1687 mm. The objectedge in an image is extracted at the single pixel level by image processing. The image errors in the directions of u and v are assumed to be the same. That means Δω =Δ[varepsilon] =1/2 pixel. The coordinates of x and y are fixed at x=y=100 mm. Thus, the relationship between measured distance and the vision computingcomprehensive error can be obtained as shown in Figure 7 [Figure omitted. See Article Image.]. The longer the distance between tested point and camera CCDs, the greater the error. In fact, at the working range of robot and the poses of the work-piece in the experiments have significant influence on the effective common view field, so the working range must be in 150-450 mm rangeThe comprehensive error changes along the x-axis as well. In Figure 6 [Figure omitted. See Article Image.], R is a point with the same height but different x coordinates compared to P. We can see αl and αr are change with xand therefore the error increases. However, it is difficult to determine whether this error becomes greater or smaller with x. As shown in Figure 8 [Figure omitted. See Article Image.], the error change tendency is obtained when z=200. From Figure 8 [Figure omitted. See Article Image.], we can see that the error decreases to a minimum when x is close to b/2. In other words, test points close to the midpoint of the line connecting two centers of the cameras produce the smallest error. Also, the error along x changes smoothly and is less in range compared with the error along z-direction.An experiment was designed for a further verification of the theory above. We keep the imaging pose and test point invariant. The cameras are raised with the robot in 50 mm increments within effective view field and images pairs are captured simultaneously. We measure the distance within the chessboard using the method as shown in Figure 3 [Figure omitted. See Article Image.], which is used for error comparison. Threechessboards were measured in this experiment. Table III [Figure omitted. See Article Image.] shows the results. The measured distance is the distance between the tested point and robot controlling point (TCP). The measured distance has a fixed transformation relationship with z between tested point and cameras. It is thus clear that error becomes greater with increasing measuring distance. This result is consistent with the theoretical analysis but does not exactly correspond numerically to the true value because we simplified the analysis model which is quite complicated in the system of practical configuration.Similarly, the robot is fixed to a certain pose at a measuring height of 250 mm. We then put the chessboard on workbench and let one side of it be as parallel as possible to the line connecting the two optical centers in xOz plane. The midpoint of the line is taken as reference. Then we measure the distance of two chessboard squares in the x positive direction. As shown in Figure 9 [Figure omitted. See Article Image.], we compute the distance of a standard square from the middle of baseline along x positive direction. The comparison of calculated and standard result values is showed in Table IV [Figure omitted. See Article Image.]. The measured distance from the middle of baseline along the x negative direction is similar to that of positive x-direction and is not repeated here. The seasuring sequence within the chessboad is marked in Figure 9 [Figure omitted. See Article Image.]. The zeroth measurement represents the result of midpoint of baseline. The change tendency of the experimental data is same as the theoretical. The reason for the difference in specific values is due to the error approximation technique similar to the total error caused by the changing of z. The experimental data in Table IV [Figure omitted. See Article Image.] cannot fully reflect the relationship shown in Figure 8 [Figure omitted. See Article Image.]. For example, the errors of data in group 2 and 5 are both smaller than their former values. This is because the error on x-direction is very small. We can see that every 50 mm increase in x leads to only an error change of 0.01 mm in Figure 8 [Figure omitted. See Article Image.] and the relative error is quite small. Therefore, error caused by changing x can be ignored if z is constant in the effective FOV.4 Discussions and conclusion4.1 DiscussionsThe research focus on the experimental technique and model analysis, which also are used guide the design of vision system and 3D path planning and the guiding of start welding position. Figure 10 [Figure omitted. See Article Image.] shows a series of images captured from a video of 3D guiding of start welding position and path planning. The cameras can rotate with the GTA welding torch, which allow the operator to adjust the cameras to a proper angel like we analyzed in the paper. First, the binocular vision system is setup according to the analysis of the above model. Second, the cameras move follow with the robot and capture two pictures (Figure 10(a) [Figure omitted. See Article Image.]). Then the 3D coordinates of start weldin position and the welding path information is calculated (Figure 10(b) [Figure omitted. See Article Image.]) and sent to the robot to control the moving of welding robot (Figure 10(c) and (d) [Figure omitted. See Article Image.]). In the guiding of start welding position and path plan, the analysis results and methods helps to determine the setup configuration, image capturing pose and image processing accuracy. For example, when the distance from camera to work-piece are in the range 150-450 mm, the maximum error is less than 1.1 mm, but if the distance is in the range of 460-600, the maximum error for same work-piece is about 1.5 mm. The research about the analysis above guide us get better results. The detail for the application in the guiding field can be partly got from our paper ([8] Chen and Chen, 2010), the paper shows us partly the error in different visual scene and experiments.4.2 ConclusionIt is a typical application to compute 3D weld seam information by stereo vision. For the welding robot, it is very common to calculate the spatial information of weld seam by fixing two cameras on the end effector. We focused on an accuracy analysis model and experimental method in order to guide research in intelligent robot welding field. The effect of vision configuration and robot accuracy on vision computing accuracy is analyzed by the general model built in this paper. The designed experimental results show that robot deviations have different effects on vision computing. Standard error of vision computing caused by repeatability positioning。
机器视觉论文好发表
机器视觉论文好发表科技改变生活，人工智能是未来世界发展的方向，机器视觉是人工智能的一个分支，写机器视觉领域的论文发表数量逐年递增，有一些初次发表论文的作者，不知道如何发表论文以及论文好发表吗，如果你是其中之一，可以先了解初次发表论文有什么要注意的，机器视觉论文发表交给发表学术论文网就够了，论文范文、发表期刊推荐一应俱全，下面就来了解一下机器视觉已经发表过的论文和发表期刊吧。
《基于机器视觉检测的码垛机器人控制系统设计》发表在《包装工程》期刊2019年第3期，该刊是cas、北大核心期刊，半月刊周期发行，部分论文摘要内容：为了提高码垛机器人对码放物品的自我分辨能力,提高码放效率,提出一种基于机器视觉检测的包装码垛机器人控制系统。
方法首先分析机器视觉码垛机器人工作过程,基于工业控制计算机和图像采集卡设计码垛机器人控制系统,提出控制系统的硬件设计和软件设计。
《基于机器视觉的车型自动分类算法设计》发表在《电子测试》期刊2019年第1期，该刊是北京自动测试技术研究所主办，半月刊周期发行，部分论文摘要内容：车型分类是智能交通系统的重要组成部分,为实现车型自动分类,本文主要针对轿车、货车和客车的分类,设计了以车辆外形尺寸为特征的基于机器视觉的自动分类算法。
该算法首先对车辆图像进行灰度化、背景差分、平滑、分割等预处理;然后提取顶长比、前后比等特征参量进行自动分类。
机器视觉论文好发表联系编辑微信：LunwenFz《机器视觉中瓶形零件母线检测方法研究》发表在《工业控制计算机》期刊2019年第1期，部分论文摘要内容：工业生产中,瓶体零件母线的检测均是人工通过特制的模具间接检测。
探讨了基于机器视觉的母线检测方法,即通过图像边界检测确定零件母线位置、重构标称母线、比较母线与标称母线、计算其与标称母线的吻合度,最后根据设定的阈值,判定零件母线部分合格与否。
此外还有《基于机器视觉的餐盘检测定位系统的研究》、《一种机器视觉的图书书标智能识别系统设计》、《机器视觉在纺织中的应用现状与发展趋势》、《基于机器视觉的滚动轴承滚动体检测》等大量的机器视觉论文范文，由此也看出，能够发表机器视觉论文的期刊有很多，安排这类论文发表还是比较容易的。
《基于机器视觉的人体行为识别算法研究》
《基于机器视觉的人体行为识别算法研究》一、引言随着人工智能技术的快速发展，人体行为识别在众多领域中扮演着越来越重要的角色。
基于机器视觉的人体行为识别算法，通过分析图像或视频中的人体运动信息，实现对人体行为的自动识别与理解。
本文旨在研究基于机器视觉的人体行为识别算法，分析其原理、方法及优缺点，为相关领域的研究与应用提供参考。
二、人体行为识别的基本原理基于机器视觉的人体行为识别算法主要依赖于图像处理和计算机视觉技术。
其基本原理包括以下几个步骤：1. 图像获取：通过摄像头等设备获取包含人体行为的视频或图像。
2. 预处理：对获取的图像进行去噪、增强等预处理操作，以便后续分析。
3. 特征提取：从预处理后的图像中提取出与人体行为相关的特征，如形状、轮廓、运动轨迹等。
4. 行为识别：根据提取的特征，运用机器学习、深度学习等算法对人体行为进行识别与分类。
三、常见的人体行为识别算法1. 基于模板匹配的算法：通过预先定义的行为模板，与实时获取的图像进行匹配，从而识别出人体行为。
该算法简单易行，但准确率受模板质量影响较大。
2. 基于深度学习的算法：利用深度神经网络学习大量数据中的特征，实现对人体行为的自动识别。
该算法具有较高的准确率，但需要大量训练数据和计算资源。
3. 基于光流法的算法：通过计算图像中像素点的运动轨迹，得到光流场，进而分析人体行为。
该算法能够较好地处理动态背景和复杂行为，但计算量大，实时性较差。
四、研究现状及优缺点分析1. 研究现状：目前，基于机器视觉的人体行为识别算法在学术界和工业界均得到了广泛关注。
随着深度学习等技术的发展，算法的准确率和鲁棒性得到了显著提高。
然而，在实际应用中仍存在诸多挑战，如环境变化、光照条件、遮挡等。
2. 优点：基于机器视觉的人体行为识别算法具有非接触式测量、实时性、高精度等优点，可广泛应用于智能监控、人机交互、运动分析等领域。
3. 缺点：算法在复杂环境下的鲁棒性仍有待提高，同时计算资源消耗较大，实时性有待进一步提高。
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百度文库 1 机器视觉技术综述
课题：机 械 工 程 测 试 技 术 班级：13 机 设 一 班
姓名：李 * 学号：1 3 1 0 1 0 0 5 1 0
目录 一.机器视觉概念和系统组成 2
1.机器视觉概念 2 百度文库 2 2.机器视觉系统组成 2
二.机器视觉主要技术 4
1.光源选择. 4
2.图像传感器技术 5
3.数字图像处理技术 8
三.应用案例 13
1. 滤光片表面缺陷检测 13
2. 磁性材料表面缺陷检测 14
3. 齿轮表面缺陷检测 14
一.机器视觉概念和系统组成 1. 机器视觉概念 机器视觉就是用机器来代替人眼做测量和判断的系统，它通过百度文库 3 光学装置和非接触传感器自动获取目标对象的图像，并由图像处理设备根据所得图像的像素分布、亮度和颜色等信息进行各种运算处理和判别分析，以提取所需的特征信息或根据判别分析结果对某些现场设备进行运动控制。机器视觉系统中的图像处理设备一般都采用计算机，所以机器视觉有时也称为计算机视觉。 2.机器视觉系统组成 一个典型的机器视觉系统包括：光源、镜头、相机（CCD或COMS相机）、图像采集卡、图像处理软件等。在搭建视觉系统时，用户需采购系统中的各个组件，但市场上机器视觉产品及设备生产厂家多数只生产其中的部分原件，如AVT的工业摄像机、Computar的工业镜头、CCS的光源等。在这种状况下，组建机器视觉系统需要大量的时间与精力来选购不同厂家的产品，无论是在人力还是资源成本上都会有更多的付出。
图表一：机器视觉系统组成框图 百度文库
4 图表二：机器视觉系统组成示意图
一. 机器视觉主要技术 1. 光源选择 光源选择是为了将被测物体与背景尽量明显分别，获得高品质、高对比度的图像。 光源的种类分为：高频荧光灯、 光纤卤素灯、LED（发光二极管）照明。它们各自的特点是： a. 高频荧光灯：使用寿命约1500－3000小时 百度文库 5 优点：扩散性好、适合大面积均匀照射 缺点：响应速度慢，亮度较暗 b. 光纤卤素灯：使用寿命约1000小时 优点：亮度高 缺点：响应速度慢，几乎没有光亮度和色温的变化。 c. LED灯：使用寿命约10000－30000小时，可以使用多个LED达到高亮度，同时可组合不同的形状，响应速度快，波长可以根据用途选择 。 选择LED光源的优势： •可制成各种形状、尺寸及各种照射角度； •可根据需要制成各种颜色，并可以随时调节亮度； •通过散热装置，散热效果更好，光亮度更稳定； •使用寿命长（约3万小时，间断使用寿命更长）； •反应快捷，可在10微秒或更短的时间内达到最大亮度； •电源带有外触发，可以通过计算机控制，起动速度快，可以用作频闪灯； •运行成本低、寿命长的LED，会在综合成本和性能方面体现出更大的优势； •可根据客户的需要，进行特殊设计。 2. 图像传感器技术 通过让机器具有某种可视的能力，制造商们获得了一种有力的百度文库 6 质量控制工具。机器视觉系统可捕获图像并可以测量一件产品的尺寸、位置和颜色、零部件的位置或者其它的关键特性，从而在无人看管的情况下提供快速“通过／未通过”判断。 所有的机器视觉系统都带有一台摄像机、一个计算机和捕捉图像并进行分析的软件。所选用的系统部件必须能符合具体应用的需要。因为图像传感器确定了成像系统的速度和分辨率，故正确的图像传感器的选取对于视觉应用的成功来说具有关键性影响。下面是机器视觉图像传感器的各种分类： a.线阵式图像传感器 一个线阵式图像传感器（逐线扫描）包含一条或者多条像素直线阵列。每个阵列与至少一个读出装置及放大器耦合。线阵图像传感器适用于那些要对连续制造的产品（如传送带上的ＰＣ板，未来的印刷塑性电路板以及其它薄型、卷状的产品，如杂志、印刷布）进行成像的机器视觉应用。总而言之，线阵式传感器总体结构简单，适用于对扁平、快速移动的物体的成像，但在需要捕获３Ｄ物体图像的应用中它们往往无法与面积型传感器相竞争。 b.全帧式传感器 全帧式传感器将光电敏感与读出结合起来。由于不存在单独的存储区，故需要一个外部的快门（或者同步频闪照明）防止入射光在任何电荷转移发生前照亮像素。如果不采用快门或频闪 ，则图像会出现拖尾污迹效果。 在机器视觉历史的早期（上世纪８０年代中期），人们采用的百度文库 7 是全帧面积传感器，因为对于该应用而言它们是唯一一种分辨率足够高的产品。如果新品应用一项应用需要１０２４×１０２４像素传感器所能提供的分辨率的话，则全帧式传感器是唯一的选择。 总而言之，全 帧传感器的体系结构是各种面积型传感器中最简单的，其分辨率和光敏感面积的密度也是最高的（后者是指其填充因数最高）。它们还提供了很高的全阱容量、低噪声和大的动态范围。不过它们需要一个机械快门。 c.帧传输式图像传感器 一个帧传输图像传感器类似于全 帧成像器。不过，它采用了第二个面阵列，该阵列实现了光屏蔽且作为图像的存储区（参见图４）。该结构并不需要一个机械快门，故 帧速率高于全 帧传感器，因为它们可以在传送一幅图像的同时获取另一幅图像。不过，由于积分仍然发生在图像转移到存储区的过程中，故图像存在拖尾污迹，性能受到一定的影响。因为要实现这一架构需要把集成电路面积增加一倍，故 帧传输图像传感器一般分辨率较低，而成本高于全 帧图像传感器。 总而言之，帧传输传感器具有更高的填充因数、更高的全阱容量、低噪声、大动态范围、电子快门和较好的帧速率。它们的主要缺点是曝光时间很短时会出现较大的图像污迹，而且制造成本较高。 d.线间转移传感器 在线间图像传感器中，光敏感和读出功能也是分开的。每个像百度文库 8 素被一个屏蔽了光线的垂直图像传感器包围，该传感器可以转移电荷。这使得线间传感器能在捕捉一帧图像的同时将前一幅图像移走，从而实现了内置的电子快门能力。 线间传感器的开发时间晚于全 帧和 帧传输传感器。随着线间技术的成熟，它已经能够提供机器视觉所需的更高的分辨率和更高的帧速率。 总而言之，线间转移传感器提供了百万像素 级的分辨率，以及很高的全阱容量。它们还具有低噪声、大动态范围、快门电子化、高 帧速率和低污迹等特点，可以实现短时曝光。 总之，用户希望获得更快的帧速率（为了跟上快速移动的物体）、更高的量子效率（以便在光线较弱时和／或成像时间更短时提供更多的图像）和更大的动态范围（这样可以在图像较亮或较暗的部分可以看到相对的细节）。电子快门、渐进式扫描读出和高灵敏度都是在明确何种传感器最适用于机器视觉应用时需要考虑的关键参数。应该记住的是，正是整套参数的匹配，才使得特定的一种传感器成为应用的最佳选择。 3. 数字图像处理技术 a.数字图像处理简介 数字图像处理(Digital Image Processing)即计算机图像处理，指将图像由模拟信号转化为数字信号，并利用计算机对图像进行去噪、增强、复原、分割、提取特征等处理的过程。图像经过处理后，输出的质量得到很大程度的增强，即改善了其视觉效果，又便于计算机完百度文库 9 成后续的分析、处理等。 图像是人类获取信息和交换信息的主要来源之一，图像处理已经在人类生活和工作的许多方面得到了广泛的应用并取得令人瞩目的成就，例如航空航天技术、通信工程、生物医学工程、工业检测、文化艺术、军事安全、电子商务、视频和多媒体系统等领域，图像处理已经成为一门前景远大的新型学科。数字图像处理技术虽然已经取得了很多重要的研究成就，但是仍然存在一些困难：（1）信息处理量大。数字图像处理的信息基本上都是以二维形式存在，处理信息量较大，对计算机的速度、存储量等有比较高的要求。（2）频带占用宽。在图像成像、传输、显示等环节的实现上，成本高，技术实现难度大，这就要求更高的频带压缩技术。（3）像素相关性较大。数字图像中每个像素并不是独立的，很多像素有着相同或者接近的灰度，相关性较大，因此信息压缩有很大地提升空间。（4）不能复现有关三维景物的所有几何信息。图像是三维景物的二维投影，所以必须附加新的测量或者合适的假定才能理解和分析三维景物。（5）人为因素的影响大。经过数字图像处理的图像一般是被人观察和分析的，人的视觉系统很复杂，机器视觉系统同样是模仿人的视觉，人的感知机理制约着机器视觉系统的研究。 在工业生产自动化过程中，数字图像处理技术是实现产品实时监控和故障诊断分析最有效的方法之一，随着计算机软硬件、思维科学研究、模式识别以及机器视觉系统等相关技术和理论的进一步发展，将促进这一方法向更高、更深层次发展。 百度文库 10 b. 数字图像处理的工具 数字图像处理的应用工具有很多，总体可以分为三类： 第一类工具的共同点是先把图像变换到其他域中进行处理，再变换到原域中进行下一步处理，例如有关图像滤波和正交变换等方法。 第二类工具是直接在空间域中进行图像处理，例如微分方程方法、统计方法等数学方法。 第三类工具和通常在空间域和频域使用的方法不同，是建立在随机集合和积分几何论基础上的运算，例如数学形态运算方法。
c.数字图像处理的研究内容 数字图像处理的研究内容主要有以下几个方面： 1．图像变换。为了得到更加简单和方便处理的图像函数，一般要对图像进行图像变换，图像变换的形式主要有光学和数字两种，分别对应连续函数和二维离散运算。常用的方法有傅立叶变换、沃尔什-哈达玛变换、离散卡夫纳－勒维变换等间接处理技术。 2．图像增强和复原。其目的都是改善图像的质量，提高图像的清晰度。图像增强可以突出预处理图像中所感兴趣信息，常用方法有灰度变换、直方图处理、锐化滤波等。图像复原可以复原被退化的图像，常采用滤波复原的方法。 3．图像压缩。这种技术可以除去冗余数据，减少描述图像所需的数