Efficient contrast invariant stereo correspondence
计算机视觉代码合集
计算机视觉是结合了传统摄影测量，现代计算机信息技术、人工智能等多学科的一个大学科，是一片开垦不足的大陆，路很远，但很多人都在跋涉！
本文转自CSDN（地址/whucv/article/details/7907391），是一篇很好的算法与代码总结文档，转载在此供大家学习参考。
http://cvlab.epfl.ch/research/detect/brief/
Dimension Reduction
Diffusion maps
/~annlee/software.htm
Dimension Reduction
Dimensionality Reduction Toolbox
http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html
Shared Matting
E. S. L.Gastaland M. M. Oliveira, Computer Graphics Forum, 2010
http://www.inf.ufrgs.br/~eslgastal/SharedMatting/
Alpha Matting
Bayesian Matting
Camera Calibration
EpipolarGeometry Toolbox
G.L.Mariottini, D.Prattichizzo, EGT: a Toolbox for Multiple View Geometry and VisualServoing, IEEE Robotics & Automation Magazine, 2005
cv研究方向及综述
cv研究方向及综述
计算机视觉（CV）是一个涉及多个子领域的学科，包括图像分类、目标检测、图像分割、目标跟踪、图像去噪、图像增强、风格化、三维重建、图像检索等。
1.图像分类：多类别图像分类、细粒度图像分类、多标签图像分类、实
例级图像分类、无监督图像分类等。
2.目标检测：吴恩达机器学习object location目标定位，关键在于将全
连接层改为卷积层。
3.图像分割：使用深度学习进行图像分割，包括全卷积像素标记网络，
编码器-解码器体系结构，多尺度以及基于金字塔的方法，递归网络，视觉注意模型以及对抗中的生成模型等。
4.目标跟踪：基于滤波理论、运动模型、特征匹配等多种方法的混合跟
踪算法研究，以及基于深度学习的目标跟踪算法研究。
5.图像去噪：比较研究不同深度学习技术对去噪效果的影响，包括加白
噪声图像的CNN、用于真实噪声图像的CNN、用于盲噪声去噪的CNN和用于混合噪声图像的CNN等。
6.图像增强：通过对图像进行变换、滤波、增强等操作，改善图像的视
觉效果或者提取更多的信息，例如超分辨率技术。
7.风格化：通过将一种艺术风格应用到图像上，改变其视觉效果。
8.三维重建：从二维图像中恢复三维场景的过程。
9.图像检索：基于内容的图像检索（CBIR），通过提取图像的特征，
进行相似度匹配，实现图像的检索。
总的来说，CV是一个充满活力的领域，涉及的研究方向非常广泛。
随着深度学习技术的发展，CV领域的研究和应用也取得了很大的进展。
低照度短波红外图像增强算法
第39卷第6期2020年12月Vol. 39 No. 6December 2020红外与毫米波学报J. Infrared Millim. Waves文章编号：1001-9014(2020)06-0818-07DOI ：10. 11972/j. issn. 1001-9014. 2020. 06. 022低照度短波红外图像增强算法张瑞3,汤心溢"，李争23(1.中国科学院大学,北京100049;2.中国科学院上海技术物理研究所，上海200083；3.中国科学院红外探测与成像技术重点实验室，上海200083)摘要：为了拓展非制冷短波红外探测器在弱光夜视观测方面的应用，开展了针对短波红外低照度成像的研究。
提 出了 一种新的图像增强方法抑制图像噪声增强图像细节进而改善图像质量。
使用3D 降噪(3DNR (3D Noise reduction))算法，将多尺度高斯差分法结合边缘保持滤波器最大限度地分离图像高频信息与隐藏噪声，再针对图像 进行自适应灰度映射。
实验结果表明：该算法显著地抑制了在低照度下图像的时域噪声，丰富了短波红外图像的细节，改善了短波红外的夜视显示效果。
关 键 词：低照度；短波红外；视网膜模型；图像增强；降噪中图分类号:TP3-05 文献标识码：AResearch on low illumination shortwave infrared imageenhancement algorithmZHANG Rui 1，2，3 , TANG Xin -Yi 2，3* , LI Zheng 2，3(1. University of Chinese Academy of Sciences , Beijing 100049 China ;收稿日期：2019- 06- 18 ,修回日期：2020- 09- 10基金项目：十三五预研课题(H J J2019-0089)Foundation items : Pre -research project of the 13th five year plan作者简介(Biography ):张瑞,博士，主要从事短波红外"成像研究工作.* 通讯作者(Corresponding author ) ： E -mail : tangxini@189. cn2. Shanghai Institute of Technical Physics , Chinese Academy of Sciences , Shanghai 200083 China ;3. key Laboratory of Infrared System Detection and Imaging Technology Shanghai 200083 China )Abstract : In order to expand application of uncooled short wave infrared array detectors for low-light night vision , a re search on low-light imaging of short-wave infrared have carried out. This paper proposes a new image enhancement method to suppress image noise enhance image details and improve image quality. The proposed schemes use 3DNR(3D noise reduction ), combine the multi-scale Gaussian differential method with the edge preserving filter to separate the high-frequency information and hidden noise of the image to the maximum extent and then carry out the adaptive grayscale mapping for the image. The experimental results demonstrate that the proposed algorithm outperforms some state-of-the-art algorithms and it can achieve outstanding image enhancement performance and suppress the time-do main noise of the image under low-light illumination.Key words : low illumination , short wave infrared (SWIR ) , retinex model , image enhancement , noise reductionPACS ：07.05. Pj引言短波InGaAs 红外成像技术工作波段主要在0. 9-1.7 ^m ,其成像模式以反射为主。
优美斯（Optimax Systems）的相位平移干扰光学测量方法白皮书说明书
The Effect Of Phase Distortion On InterferometricMeasurements Of Thin Film Coated Optical SurfacesJon Watson, Daniel SavageOptimax Systems, 6367 Dean Parkway, Ontario, NY USA*********************©Copyright Optimax Systems, Inc. 2010This paper discusses difficulty in accurately interpreting surface form data from a phase shifting interferometer measurement of a thin film interference coated surfaces.PHASE-SHIFTING INTERFEROMETRYPhase-shifting interferometry is a metrology tool widely used in optical manufacturing to determine form errors of an optical surface. The surface under test generates a reflected wavefront that interferes with the reference wavefront produced by the interferometer 1. A phase-shifting interferometer modulates phase by slightly moving the reference wavefront with respect to the reflected test wavefront 2 . The phase information collected is converted into the height data which comprises the surface under test3.Visibility of fringes in an interferometer is a function of intensity mismatch between the test and reference beams. Most commercially available interferometers are designed to optimize fringe contrast based on a 4% reflected beam intensity. If the surface under test is coated for minimum reflection near or at the test wavelength of the interferometer, the visibility of the fringe pattern can be too low to accurately measure.OPTICAL THIN-FILM INTERFERENCE COATINGSOptical thin-film interference coatings are structures composed of one or more thin layers (typically multiples of a quarter-wave optical thickness) of materials deposited on the surface of an optical substrate.The goal of interference coatings is to create a multilayer film structure where interference effects within the structure achieve a desired percent intensity transmission or reflection over a given wavelength range.The purpose of the coating defines the design of the multilayer structure. Basic design variables include:• Number of layers• Thickness of each layer• Material of each layerThe most common types of multilayer films are high reflector (HR) and anti-reflection (AR) coatings. HR coatings function by constructively interfering reflected light, while AR coatings function by destructively interfering reflected light. These coatings are designed to operate over a specific wavelength range distributed around a particular design wavelength.To produce the desired interference effects, thin-film structures are designed to modulate the phase of the reflected or transmitted wavefront. The nature of the interference effect depends precisely on the thickness of each layer in the coating as well as the refractive index of each layer. If the thickness and index of each layer is uniform across the coated surface, the reflected wavefront will have a constant phase offset across the surface. However, if layer thicknesses or index vary across the coated surface, then the phase of thereflected wavefront will also vary. Depending on the design of the coating and the severity of the thickness or index non-uniformity, the distortion of the phase of the reflected wavefront can be severe. 4Layer thickness non-uniformity is inherent in the coating process and is exaggerated by increasing radius of curvature of the coated surface.5 All industry-standard directed source deposition processes (thermal evaporation, sputtering, etc) result in some degree of layer thickness non-uniformity.5 Even processes developed to minimize layer non-uniformity, such as those used at Optimax, will still result in slight layer non-uniformity (within design tolerance).TESTING COATED OPTICS INTERFEROMETRICALLYPhase-shifting interferometers use phase information to determine the height map of the surface under test. However, surfaces coated with a thin-film interference coating can have severe phase distortion in the reflected wavefront due to slight layer thickness non-uniformities and refractive index inhomogeneity. Therefore, the measured irregularity of a coated surface measured on a phase shifting interferometer at a wavelength other than the design wavelength, may not represent the actual irregularity of the surface. Even using a phase shifting interferometer at the coating design wavelength does not guarantee accurate surface irregularity measurements. If a coating has very low reflectance over any given wavelength range (such as in the case of an AR coating), the phase shift on reflection with wavelength will vary significantly in that range.7 Figure 1 shows an example of how the phase can vary with coating thickness variations.Figure 1In this particular case, if a point at the lens edge has the nominal coating thickness and the coating at lens center is 2% thicker, expect ~38° phase difference in the measurement (~0.1 waves). This will erroneous be seen as height by the interferometer, despite the actual height change in this case being less than 7nm (~0.01 waves). Also, depending on coating design, low fringe visibility may inhibit measurements.There is an extreme method to determine the irregularity of a thin-film interference coated surface by flash coating it with a bare metal mirror coating. A metal mirror coating is not a thin-film interference coating, and the surface of the mirror represents the true surface, This relatively expensive process requires extra time, handling, and potential damage during the metal coating chemical strip process.CONCLUSIONS•There can be practical limitations to getting accurate surface form data on coated optical surfaces due to issues with phase distortion and fringe visibility.•The issues are a function of thin film coating design particulars and the actual deposition processes.1 R.E. Fischer, B. Tadic-Galeb, P. Yoder, Optical System Design, Pg 340, McGraw Hill, New York City, 20082 H.H. Karow, Fabrication Methods For Precision Optics, Pg 656, John Wiley & Sons, New York City, 19933 MetroPro Reference Guide OMP-0347J, Page 7-1, Zygo Corporation, Middlefield, Connecticut, 20044 H.A. Macleod, Thin Film Optical Filters, Chapter 11: Layer uniformity and thickness monitoring, The Institute of Physics Publishing, 2001.5 R.E. Fischer, B. Tadic-Galeb, P. Yoder, Optical System Design, Pg 581, McGraw Hill, New York City, 2008。
计算机视觉的图像质量评价方法(七)
计算机视觉的图像质量评价方法随着人工智能和计算机视觉技术的飞速发展，图像质量评价方法变得越来越重要。
对于图像处理和图像识别领域来说，如何准确评价图像的质量对于算法的优化和应用的效果有着至关重要的影响。
在这篇文章中，我们将探讨一些常见的计算机视觉的图像质量评价方法。
一、主观评价方法主观评价方法是指人类观察者通过肉眼直接对图像进行评价的方法。
这种评价方法的优点在于能够直观反映图像质量，但缺点是受到主观因素和个体差异的影响。
在实际应用中，主观评价方法通常需要进行大量的实验，以获取更为客观的结果。
最常见的主观评价方法是MOS（Mean Opinion Score）方法，即通过对一组观察者进行一定数量的实验，然后对他们的评价进行平均，来得到图像质量的评分。
二、客观评价方法客观评价方法是指通过计算机算法对图像进行评价的方法。
这种方法的优点在于能够快速、准确地评价大量的图像，但缺点是往往难以完全模拟人类的感知过程。
常见的客观评价方法包括 PSNR（Peak Signal-to-Noise Ratio）、SSIM （Structural Similarity Index）、VIF（Visual Information Fidelity）等。
这些方法都是基于对比原始图像和处理后图像的像素值的差异来评价图像质量的。
然而，这些方法往往难以准确地捕捉到人类对图像质量的真实感知。
三、混合评价方法混合评价方法是指结合主观评价和客观评价的方法。
这种方法的优点在于能够兼顾到图像质量的客观度和主观度，但缺点是需要较大的成本和复杂的实验设计。
在实际应用中，研究者往往会结合主观评价和客观评价的方法，来得到更为全面的图像质量评价结果。
四、新兴评价方法随着深度学习和神经网络技术的发展，一些新兴的图像质量评价方法也开始受到关注。
基于深度学习的图像质量评价方法能够模拟人类的感知过程，能够更准确地评价图像的质量。
同时，一些基于强化学习的图像质量评价方法也开始出现，这些方法能够根据实际应用场景的反馈来不断优化评价模型，进一步提高评价的准确度。
基于深度学习的智能车辆视觉里程计技术发展综述
2021年第1期【摘要】针对基于模型的视觉里程计在光照条件恶劣的情况下存在鲁棒性差、回环检测准确率低、动态场景中精度不够、无法对场景进行语义理解等问题，利用深度学习可以弥补其不足。
首先，简略介绍了基于模型的里程计的研究现状，然后对比了常用的智能车数据集，将基于深度学习的视觉里程计分为有监督学习、无监督学习和模型法与深度学习结合3种，从网络结构、输入和输出特征、鲁棒性等方面进行分析，最后，讨论了基于深度学习的智能车辆视觉里程计研究热点，从视觉里程计在动态场景的鲁棒性优化、多传感器融合、场景语义分割3个方面对智能车辆视觉里程计技术的发展趋势进行了展望。
主题词：视觉里程计深度学习智能车辆位置信息中图分类号：U461.99文献标识码：ADOI:10.19620/ki.1000-3703.20200736Review on the Development of Deep Learning-Based Vision OdometerTechnologies for Intelligent VehiclesChen Tao,Fan Linkun,Li Xuchuan,Guo Congshuai（Chang ’an University,Xi ’an 710064）【Abstract 】Visual odometer can,achieve with deep learning,better performance on robustness and accuracy through solving the problems such as the weak robustness under poor illumination,low detection accuracy in close loop and insufficient accuracy in dynamic scenarios,disability in understanding the scenario semantically.Firstly,this paper briefly introduces the research status of the model-based odometer,then compares the commonly-used intelligent vehicle datasets,and then divides the learning-based visual odometer into supervised learning,unsupervised learning and hybrid model which combines model-based with deep learning-based model.Furthermore,it analyzes the learning-based visual odometer from the aspects of network structure,input and output characteristics,robustness and so on.Finally,the research hotspots of learning-based visual odometer for intelligent vehicle are discussed.The development trend of learning-based visual odometer for intelligent vehicle is discussed from 3aspects which respectively are robustness in dynamic scenarios,multi-sensor fusion,and scenario semantic segmentation.Key words:Visual odometer,Deep learning,Intelligent vehicle,Location information陈涛范林坤李旭川郭丛帅（长安大学，西安710064）*基金项目：国家重点研发计划项目（2018YFC0807500）；国家自然科学基金面上项目（51978075）。
立体匹配的原理和方法
立体匹配的原理和方法Stereo matching is a fundamental problem in computer vision that aims to establish correspondences between points in a pair of stereo images. 立体匹配是计算机视觉中的一个基本问题，旨在建立一对立体图像中点的对应关系。
It is a crucial step in tasks such as depth estimation, visual odometry, and 3D reconstruction. 这是深度估计、视觉里程计和三维重建等任务中的一个关键步骤。
The principle of stereo matching is to find corresponding points in two images taken from different viewpoints. 立体匹配的原理在于找出来自不同视角拍摄的两幅图像中对应的点。
By comparing these points, the depth information of the scene can be inferred. 通过比较这些点，可以推断出场景的深度信息。
One common method for stereo matching is the use of pixel-based matching algorithms. 一个常见的立体匹配方法是使用基于像素的匹配算法。
These algorithms compare the intensity or color of pixels in the two images to find correspondences. 这些算法比较两幅图像中像素的强度或颜色来找到对应的点。
However, pixel-based methods often struggle with handling textureless regions or occlusions in the images. 然而，基于像素的方法常常难以处理图像中无纹理区域或遮挡。
视觉单词优化仿射尺度不变特征变换的视频人脸识别
视觉单词优化仿射尺度不变特征变换的视频人脸识别
张雪峰;赵莉
【期刊名称】《计算机应用与软件》
【年(卷),期】2015(032)007
【摘要】针对视频人脸识别中由于光照、表情、姿态等变化而影响识别性能的问题,提出一种基于视觉单词优化仿射尺度不变特征变换的视频人脸识别算法.首先从兴趣点提取仿射尺度不变特征变换的图像描述符,将其作为人脸图像表示法;然后,由高斯差分检测,使用视觉单词的索引取代这些描述符;最后,计算视觉单词之间的巴氏距离,并利用最大相似性原则完成识别.在两大通用视频人脸数据库Honda及MoBo上的实验验证了该算法的有效性.实验结果表明,相比其他几种较为先进的视频人脸识别算法,该算法明显提高了识别率,并且大大降低了计算复杂度,有望应用于实时视频人脸识别系统.
【总页数】5页(P223-227)
【作者】张雪峰;赵莉
【作者单位】信阳农林学院计算机科学系河南信阳464000;信阳农林学院计算机科学系河南信阳464000
【正文语种】中文
【中图分类】TP391
【相关文献】
1.改进的抗全仿射尺度不变特征变换图像匹配算法 [J], 贺柏根;朱明
2.仿射高斯尺度空间下的完全仿射不变特征提取 [J], 李威;史泽林;尹健
3.基于尺度不变特征变换优化算法的带遮挡人脸识别 [J], 周玲丽;赖剑煌
4.改进仿射尺度不变特征变换算法的图像配准 [J], 范雪婷;张磊;赵朝贺
5.基于仿射尺度不变特征变换的掌纹识别 [J], 苑玮琦;林森;吴微;方婷
因版权原因，仅展示原文概要，查看原文内容请购买。
High accuracy stereo depth maps using structured light(IEEE)
High-Accuracy Stereo Depth Maps Using Structured LightDaniel Scharstein Middlebury College schar@Richard Szeliski Microsoft Research szeliski@AbstractRecent progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms,moti-vating the need for more challenging scenes with accurate ground truth information.This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using struc-tured light.Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors.We present new stereo data sets acquired with our method and demon-strate their suitability for stereo algorithm evaluation.Our results are available at /stereo/.1.IntroductionThe last few years have seen a resurgence of interest in the development of highly accurate stereo correspondence algorithms.Part of this interest has been spurred by funda-mental breakthroughs in matching strategies and optimiza-tion algorithms,and part of the interest is due to the exis-tence of image databases that can be used to test and com-pare such algorithms.Unfortunately,as algorithms have improved,the difficulty of the existing test images has not kept pace.The best-performing algorithms can now cor-rectly match most of the pixels in data sets for which correct (ground truth)disparity information is available[21].In this paper,we devise a method to automatically acquire high-complexity stereo image pairs with pixel-accurate correspondence information.Previous approaches have either relied on hand-labeling a small number of im-ages consisting mostly of fronto-parallel planes[17],or set-ting up scenes with a small number of slanted planes that can be segmented and then matched reliably with para-metric correspondence algorithms[21].Synthetic images have also been suggested for testing stereo algorithm per-formance[12,9],but they typically are either too easytoFigure1.Experimental setup,showing the digitalcamera mounted on a translation stage,the video pro-jector,and the complex scene being acquired.solve if noise,aliasing,etc.are not modeled,or too difficult, e.g.,due to complete lack of texture in parts of the scene.In this paper,we use structured light to uniquely label each pixel in a set of acquired images,so that correspon-dence becomes(mostly)trivial,and dense pixel-accurate correspondences can be automatically produced to act as ground-truth data.Structured-light techniques rely on pro-jecting one or more special light patterns onto a scene, usually in order to directly acquire a range map of the scene,typically using a single camera and a single projector [1,2,3,4,5,7,11,13,18,19,20,22,23].Random light patterns have sometimes been used to provide artificial tex-ture to stereo-based range sensing systems[14].Another approach is to register range data with stereo image pairs, but the range data is usually of lower resolution than the images,and thefields of view may not correspond exactly, leading to areas of the image for which no range data is available[16].2.Overview of our approachThe goal of our technique is to produce pairs of real-world images of complex scenes where each pixel is labeled with its correspondence in the other image.These image pairs can then be used to test the accuracy of stereo algo-rithms relative to the known ground-truth correspondences.Our approach relies on using a pair of cameras and one or more light projectors that cast structured light patterns onto the scene.Each camera uses the structured light se-quence to determine a unique code(label)for each pixel. Finding inter-image correspondence then trivially consists offinding the pixel in the corresponding image that has the same unique code.The advantage of our approach,as compared to using a separate range sensor,is that the data sets are automatically registered.Furthermore,as long as each pixel is illuminated by at least one of the projectors,its correspondence in the other image(or lack of correspondence,which indicates oc-clusion)can be unambiguously determined.In our current experimental setup(Figure1),we use a single digital camera(Canon G1)translating on a lin-ear stage,and one or two light projectors illuminating the scene from different directions.We acquire images un-der both structured lighting and ambient illumination condi-tions.The ambient illuminated images can be used as inputs to the stereo matching algorithms being evaluated.Let us now define some terminology.We distinguish be-tween views–the images taken by the cameras–and illu-minations–the structured light patterns projected onto the scene.We model both processes using planar perspective projection and use coordinates(x,y)for views and(u,v) for illuminations.There are two primary camera views,L(left)and R (right),between which correspondences are to be estab-lished.The illumination sources from which light patterns are projected are identified using numbers{0,1,...}.More than one illumination direction may be necessary to illumi-nate all surfaces in complex scenes with occluding objects.Our processing pipeline consists of the following stages:1.Acquire all desired views under all illuminations.2.Rectify the images to obtain the usual horizontalepipolar geometry,using either a small number of cor-responding features[15]or dense2D correspondences (step4).3.Decode the light patterns to get(u,v)codes at eachpixel in each view.e the unique codes at each pixel to compute corre-spondences.(If the images are rectified,1D search can be performed,else2D search is required.)The results of this correspondence process are the(usual)view dis-parities(horizontal motion).5.Determine the projection matrices for the illuminationsources from the view disparities and the code labels.6.Reproject the code labels into the two-view geometry.This results in the illumination disparities.bine the disparities from all different sources toget a reliable and accuratefinal disparity map.8.Optionally crop and downsample the disparity mapsand the views taken under ambient lighting.The remainder of this paper is structured as follows. The next section describes the algorithms used to determine unique codes from the structured lighting.Section4dis-cusses how view disparities and illumination disparities are established and merged.Section5describes our experimen-tal results,and Section6describes our conclusions and fu-ture work.3.Structured lightTo uniquely label each pixel,we project a series of struc-tured light images onto the scene,and decode the set of pro-jected intensities at each pixel to give it a unique label.The simplest kind of pattern to project is a series of single stripe images(light planes)[3,7,19],but these require O(n)im-ages,where n is the width of the image in pixels.Instead,we have tested two other kinds of structured light:binary Gray-code patterns,and series of sine waves.3.1.Gray codesGray-code patterns only contain black and white(on/off) pixel values,which were the only possibilities available with the earliest LCD ing such binary im-ages requires log2(n)patterns to distinguish among n lo-cations.For our projector(Sony VPL-CX10)with1024×768pixels,it is sufficient to illuminate the scene with10 vertical and10horizontal patterns,which together uniquely encode the(u,v)position at each pixel.Gray codes are well suited for such binary position encoding,since only one bit changes at a time,and thus small mislocalizations of0-1changes cannot result in large code changes[20].Decoding the light patterns is conceptually simple,since at each pixel we need only decide whether it is illuminated or not.We could for example take two reference images, all-white and all-black,and compare each code pixel with the average of the two.(With a gray-level projector,we could also project a reference image with0.5intensity). Such reference images measure the albedo of each scene point.In practice,however,this does not work well due to interreflections in the scene and“fogging”inside the pro-jector(adding a low-frequency average of intensities to the projected pattern),which causes increased brightness near bright areas.We have found that the only reliable way of thresholding pixels into on/off is to project both the code pattern and its inverse.We can then label each pixel accord-ing to whether the pattern or its inverse appears brighter. This avoids having to estimate the local albedo altogether. The obvious drawback is that twice as many images are re-quired.Figure2shows examples of thresholded Gray-code images.Figure2.Examples of thresholded Gray-code im-ages.Uncertain bits are shown in gray.(Full-sizeversions of all images in this paper are available at/stereo/.)Unfortunately,even using patterns and their inverses may not be enough to reliably distinguish light patterns on surfaces with widely varying albedos.In our experiments, we have found it necessary to use two different exposure times(0.5and0.1sec.).At each pixel,we select the ex-posure setting that yields the largest absolute difference be-tween the two illuminations.If this largest difference is still below a threshold(sum of signed differences over all color bands<32),the pixel is labeled“unknown”(gray pixels in Figure2),since its code cannot be reliably determined.This can happen in shadowed areas or for surfaces with very low albedo,high reflectance,or at oblique angles.The initial code values we obtain by concatenating the bits from all the thresholded binary images need to be cleaned up and potentially interpolated,since the camera resolution is typically higher than projector resolution.In our case,the projector has a1024×768resolution,and the camera has2048×1536.Since the camera only sees a subset of the illuminated scene(i.e.,it is zoomed in)and il-lumination pixels can appear larger on slanted surfaces,we get even more discrepancy in resolution.In our setup,each illumination pixel is typically2–4camera pixels wide.We clean up the Gray code images byfilling small holes caused by unknown bit values.We then interpolate(integer)code values to get a higher resolution and avoid multiple pixels with the same code.Interpolation is done in the prominent code direction,i.e.,horizontally for u and vertically for v. We currently compute a robust average over a sliding1D window of7values.The results of the entire decoding pro-cess are shown in Figure4a.3.2.Sine wavesBinary Gray-code patterns use only two different inten-sity levels and require a whole series of images to uniquely determine the pixel code.Projecting a continuous function onto the scene takes advantage of the gray-level resolution available in modern LCD projectors,and can thus poten-tially require fewer images(or alternatively,result in greater precision for the same number of images).It can also po-tentially overcome discretization problems that might intro-duce artifacts at the boundaries of binary patterns[6].Consider for example projecting a pure white pattern and a gray-level ramp onto the scene.In the absence of noise and non-linearities,the ratio of the two values would give us the position along the ramp of each pixel.However,this ap-proach has limited effective spatial resolution[11,22].Pro-jecting a more quickly varying pattern such as a sawtooth al-leviates this,but introduces a phase ambiguity(points at the same phase in the periodic pattern cannot be distinguished), which can be resolved using a series of periodic patterns at different frequencies[13].A sine wave pattern avoids the discontinuities of a sawtooth,but introduces a further two-way ambiguity in phase,so it is useful to project two or more waves at different phasesOur current algorithm projects sine waves at two differ-ent frequencies and12different phases.Thefirst frequency has a period equal to the whole(projector)image width or height;the second has10periods per screen.Given the images of the scene illuminated with these patterns,how do we compute the phase and hence(u,v) coordinates at each pixel?Assuming a linear image forma-tion process,we have the following(color)image formation equationI kl(x,y)= A(x,y)B kl[sin(2πf k u+φl)+1],(1)where A(x,y)is the(color)albedo corresponding to scene pixel(x,y),B kl is the intensity of the(k,l)th projected pat-tern,f k is its frequency,andφl is its phase.A similar equa-tion can be obtained for horizontal sine wave patterns by replacing u with v.Assume for now that we only have a single frequency f k and let c l=cosφl,s l=sinφl,c u=cos(2πf k u), s u=sin(2πf k u),and C= A(x,y)B.The above equation can then be re-written(for a given pixel(x,y))asI kl= C[s u c l+c u s l+1].(2)We can estimate the illuminated albedo value C at each pixel by projecting a mid-tone grey image onto the scene. The above equation is therefore linear in the unknowns (c u,s u),which can be(optimally)recovered using linear least squares[10],given a set of images with different (known)phasesφl.(In the least squaresfitting,we ignore any color values that are saturated,i.e.,greater than240.) An estimate of the u signal can then be recovered usingu=p−1u(12πtan−1s uc u+m),(3)where p u=W/f u is the sine period(in pixels)and m is the (unknown)integral phase wrap count.To solve the phase wrapping problem,wefirst estimate the value of u usingFigure(c u,s u)leastsquares solutionto the constraint lines,and the ellipse around it indi-cates the two-dimensional uncertainty.a single wave(f1=1),and then repeat the estimation with f2=10,using the previous result to disambiguate the phase.Since we are using least squares,we can compute a cer-tainty for the u estimate.The normal equations for the least squares system directly give us the information matrix(in-verse covariance)for the(c u,s u)estimate.We can convert this to a variance in u by projecting along the direction nor-mal to the line going through the origin and(c u,s u)(Fig-ure3).Furthermore,we can use the distance of thefitted point(c u,s u)from the unit circle as a sanity check on the quality of our sine wavefiputing certainties allows us to merge estimates from different exposures.At present, we simply pick the estimate with the higher certainty.Figure4b shows the results of recovering the u positions using sine patterns.For these experiments,we use all12 phases(φ=0◦,30◦,...,330◦)and two different exposures (0.1and0.5sec).In the future,we plan to study how the certainty and reliability of these estimates varies as a func-tion of the number of phases used.parisonFigure4shows examples of u coordinates recovered both from Gray code and sine wave patterns.The total num-ber of light patterns used is80for the Gray codes(10bit patterns and their inverses,both u and v,two exposures), and100for the sine waves(2frequencies and12phases plus1reference image,both u and v,two exposures).Vi-sual inspection shows that the Gray codes yield better(less noisy)results.The main reason is that by projecting binary patterns and their inverses,we avoid the difficult task of es-timating the albedo of the scene.Although recovering the phase of sine wave patterns potentially yields higher reso-lution and could be done with fewer images,it is also more susceptible to non-linearities of the camera and projector and to interreflections in the scene.In practice,the time to take the imagesof allstructuredlight patterns is relatively small compared to that of setting up the scene and calibrating the cameras.We thus use the Gray code method for the results reported here.(a):Gray code(b):sine waveputed u coordinates(only low-orderbits are shown).4.Disparity computationGiven N illumination sources,the decoding stage de-scribed above yields a set of labels(u ij(x,y),v ij(x,y)),for each illumination i∈{0,...,N−1}and view j∈{L,R}. Note that these labels not only uniquely identify each scene point,but also encode the coordinates of the illumination source.We now describe how high-accuracy disparities can be computed from such labels corresponding to one or more illumination directions.4.1.View disparitiesThefirst step is to establish correspondences between the two views L and R byfinding matching code values.As-suming rectified views for the moment,this amounts to a simple1D search on corresponding scanlines.While con-ceptually simple,several practical issues arise:•Some pixels may be partially occluded(visible only in one view).•Some pixels may have unknown code values in some illuminations due to shadows or reflections.•A perfect matching code value may not exist due to aliasing or interpolation errors.•Several perfect matching code values may exist due to the limited resolution of the illumination source.•The correspondences computed from different illumi-nations may be inconsistent.Thefirst problem,partial occlusion,is unavoidable and will result in unmatched pixels.The number of unknown code values due to shadows in the scene can be reduced by us-ing more than one illumination source,which allows us to establish correspondences at all points illuminated by at least one source,and also enables a consistency check at pixels illuminated by more than one source.This is ad-vantageous since at this stage our goal is to establish only high-confidence correspondences.We thus omit all pixelswhose disparity estimates under different illuminations dis-agree.As afinal consistency check,we establish dispari-ties d LR and d RL independently and cross-check for con-sistency.We now have high-confidence view disparities atpoints visible in both cameras and illuminated by at leastone source(see Figures6b and7b).Before moving on,let us consider the case of unrectifiedviews.The above method can still be used,except that a2D search must be used tofind corresponding codes.Theresulting set of high-quality2D correspondences can thenbe used to rectify the original images[15].4.2.Illumination disparitiesThe next step in our system is to compute another setof disparities:those between the cameras and the illumina-tion sources.Since the code values correspond to the im-age coordinates of the illumination patterns,each camera-illumination pair can be considered an independent sourceof stereo disparities(where the role of one camera is playedby the illumination source).This is of course the idea be-hind traditional structured lighting systems[3].The difference in our case is that we can register theseillumination disparities with our rectified view disparities d LR without the need to explicitly calibrate the illumina-tion sources(video projectors).Since ourfinal goal is toexpress all disparities in the rectified two-view geometry,we can treat the view disparities as a3D reconstruction ofthe scene(i.e.,projective depth),and then solve for the pro-jection matrix of each illumination source.Let us focus on the relationship between the left view Land illumination source0.Each pixel whose view disparityhas been established can be considered a(homogeneous)3D scene point S=[x y d1]T with projective depthd=d LR(x,y).Since the pixel’s code values(u0L,v0L) also represent its x and y coordinates in the illumination pattern,we can write these coordinates as homogenous2D point P=[u0L v0L1]T.We then haveP∼=M0L S,where M0L is the unknown4×3projection matrix of illu-mination source0with respect to the left camera.If we letm1,m2,m3denote the three rows of M0L,this yieldsu0L m3S=m1S,andv0L m3S=m2S.(4) Since M is only defined up to a scale factor,we set m34=1. Thus we have two linear equations involving the11un-known entries of M for each pixel whose disparity and il-lumination code are known,giving us a heavily overdeter-mined linear system of equations,which we solve using least squares[10].If the underlying disparities and illumination codes arecorrect,this is a fast and stable method for computing M0L.In practice,however,a small number of pixels with largedisparity errors can strongly affect the least-squaresfit.Wetherefore use a robustfit with outlier detection by iteratingthe above process.After each iteration,only those pixelswith low residual errors are selected as input to the nextiteration.We found that after4iterations with successivelylower error thresholds we can usually obtain a very goodfit.Given the projection matrix M0L,we can now solveEquation(4)for d at each pixel,using again a least-squaresfit to combine the two estimates.This gives us the illumi-nation disparities d0L(x,y)(see Figures6c and7c).Note that these disparities are available for all points illuminatedby source0,even those that are not visible from the rightcamera.We thus have a new set of disparities,registeredwith thefirst set,which includes half-occluded points.Theabove process can be repeated for the other camera to yielddisparities d0R,as well as for all other illumination sourcesi=1...N−1.bining the disparity estimatesOur remaining task is to combine the2N+2disparitymaps.Note that all disparities are already registered,i.e.,they describe the horizontal motion between views L andR.Thefirst step is to create combined maps for each of L and R separately using a robust average at pixels with morethan one disparity estimate.Whenever there is a majority ofvalues within close range,we use the average of this subsetof values;otherwise,the pixel is labeled unknown.In thesecond step,the left and right(combined)maps are checkedfor consistency.For unoccluded pixels,this means thatd LR(x,y)=−d RL(x+d LR(x,y),y),and vice versa.If the disparities differ slightly,they are ad-justed so that thefinal set of disparity maps is fully con-sistent.Note that since we also have disparities in half-occluded regions,the above equation must be relaxed toreflect all legal visibility situations.This yields thefinal,consistent,and highly accurate pair of disparity maps relat-ing the two views L and R(Figures6d and7d).The twofinal steps are cropping and downsampling.Upto this point,we are still dealing with full-size(2048×1536)images.In our setup,disparities typically range from about210to about450.We can bring the disparities closer to zeroby cropping to the jointfield of view,which in effect stabi-lizes an imaginary point just behind farthest surface in thescene.This yields a disparity range of0–240,and an imagewidth of1840.Since most current stereo implementationswork with much smaller image sizes and disparity ranges,we downsample the images and disparity maps to quartersize(460×384).The disparity maps are downsampled us-ing a majorityfilter,while the ambient images are reducedFigure5.The two image pairs:Cones(left)and Teddy(right).with a sharp8-tapfilter.Note that for the downsampled im-ages,we now have disparities with quarter-pixel accuracy.A remaining issue is that of holes,i.e.,unknown dispar-ity values,which are marked with a special value.While small holes can befilled by interpolation during the above process,large holes may remain in areas where no illumina-tion codes were available to begin with.There are two main sources for this:(1)surfaces that are highly specular or have very low albedo;and(2)areas that are shadowed under all illuminations.Special care must be taken to avoid both sit-uations when constructing test scenes whose disparities are to be estimated with the method described here.5.ResultsUsing the method described in the previous sections,we have acquired two different scenes,Cones and Teddy.Fig-ure5shows views L and R of each scene taken under ambi-ent lighting.L and R are actually views3and7out of a total of9images we have taken from equally-spaced viewpoints, which can be used for prediction-error evaluation[21].The Cones scene was constructed such that most scene points visible from either view L and R can be illuminated with a single light source from above(see Figure6a).The exception is the wooden lattice in the upper right quadrant in the image.(Its shadow falls on a planar surface,however, so that the missing disparities could befilled in by interpo-lation.)For the Teddy we used two illumination directions (see Figure7a).Due to the complex scene,however,several small areas are shadowed under both illuminations.Figures6and7also show the recovered view disparities (b)and illumination disparities(c),as well as thefinal dis-parity maps combined from all sources(d).The combina-tion step not onlyfills in half-occluded and half-shadowed regions,but also serves to detect outlier disparities(e.g.,due to errors in the projection matrix estimation).In order to verify that our stereo data sets are useful for evaluating stereo matching algorithms,we ran several of the algorithms from the Middlebury Stereo Page[21]on our new images.Figure8shows the results of three al-gorithms(SSD with a21×21shiftable window,dynamic programming,and graph cuts)on the cropped and down-sampled image pairs,as well as the corresponding ground-truth data.Table1shows the quantitative performance in non-occluded areas(percentage of“bad”pixels with large disparity errors)for two error thresholds t=1and t=2. (We ignore points whose true disparities are unknown.) The results clearly indicate that our data sets are chal-lenging,yet not unrealistically so.Difficulties posed to the matching algorithms include a large disparity range, complex surface shapes,textureless areas,narrow occlud-ing objects and ordering-constraint violations.Of the algo-rithms tested,the graph-cut method performs best,although it clearly cannot handle some of the complex occlusion sit-uations and some highly-slanted surfaces.6.ConclusionIn this paper we have developed a new methodology to acquire highly precise and reliable ground truth dis-parity measurements accurately aligned with stereo im-age pairs.Such high-quality data is essential to evalu-ate the performance of stereo correspondence algorithms, which in turn spurs the development of even more accu-rate algorithms.Our new high-quality disparity maps and the original input images are available on our web site at /stereo/.We plan to add these new data sets to those already in use to benchmark the per-formance of stereo correspondence algorithms[21].Our novel approach is based on taking stereo image pairs illuminated with active lighting from one or more projectors.The structured lighting enables us to uniquely code each scene pixel,which makes inter-camera corre-spondence much easier and more reliable.Furthermore,the encoded positions enable the recovery of camera-projector disparities,which can be used as an auxiliary source of in-formation to increase the reliability of correspondences and tofill in missing data.We have investigated two different kinds of structured light:binary Gray codes,and continuous sine waves.At present,the Gray codes give us more reliable estimates of projector coordinates,due mainly to their higher insensitiv-ity to effects such as photometric nonlinearities and inter-reflections.In future work,we plan to develop the sine-wave approach further,to see if we can reduce the to-tal number of acquired images necessary to recover high-(a)(b)(c)(d)Figure 6.Left view of Cones (one illumination source is used):(a)scene under illumination (note absence of shadows except in upper-right corner);(b)view disparities;(c)illumination disparities;(d)final (combined)disparity map.Unknown disparities are shown in black.(a)(b)(c)(d)Figure 7.Left view of Teddy (two illumination sources are used):(a)scene under the two illuminations;(b)view disparities;(c)illumination disparities;(d)final (combined)disparity map.Unknown disparities are shown in black.SSD Dynamic progr.Graph cut Ground truthFigure 8.Stereo results on cropped and downsampled images:Cones (top)and Teddy (bottom).。
一种高分辨率3维图像的自适应降噪算法
一种高分辨率3维图像的自适应降噪算法向志聪;张程潇;白玉磊;赖文敬;王钦若;周延周【摘要】为了获得高保真3维图像，采用了一种针对高分辨率3维图像的自适应均值降噪算法。
首先使用一种由激光器、高分辨率3维相机、直线电机和计算机等设备组成的线激光高精度3维测量实验系统对自然纹理皮革进行测量。
然后针对系统测量所得的高分辨率3维自然纹理图像（每英寸点数大于1000），进行了理论分析和实验验证，取得了降噪后的高保真3维图像数据，并与传统的均值滤波、小波变换滤波的降噪效果进行对比。
结果表明，该算法能自动选取最优的降噪窗口，有效地去除3维图像的噪声信息，并保留高分辨率图像丰富的边缘、细节信息，最终得到高保真的高分辨率3维自然纹理图像。
该实验结果对于高分辨率图像的降噪问题是十分有帮助的。
%In order to obtain high-fidelity 3-D images, an adaptive mean filtering algorithm for high resolution 3-D images was proposed.Firstly, a high-precision 3-D linear laser measuring system consisting of a laser , two high-resolution 3-D cameras, two linear motors and a computer was established to measure the texture of leather .After theoretical analysis and experimental verification of the high-resolution 3-D texture images ( dots per inch>1000) collected by the measuring system , the data of high-fidelity three dimensional images after filtering were gotten .The effect of the adaptive mean filtering algorithm was compared with the effects of mean filtering method and wavelet threshold filtering method .The results show that the adaptive mean filtering algorithm can remove noise of 3-D images effectively , select the appropriate filtering window automatically , and also keepdetails and edge information of high resolution images .Finally, the high resolution 3-D texture images with high fidelity would be obtained.The experimental results are very helpful for denoising processing of high resolution images .【期刊名称】《激光技术》【年(卷),期】2015(000)005【总页数】5页(P697-701)【关键词】图像处理;高保真3维图像;自适应均值降噪;高分辨率;线激光;3维测量【作者】向志聪;张程潇;白玉磊;赖文敬;王钦若;周延周【作者单位】广东工业大学自动化学院，广州510006;广东工业大学自动化学院，广州510006;广东工业大学自动化学院，广州510006;广东工业大学自动化学院，广州510006;广东工业大学自动化学院，广州510006;广东工业大学自动化学院，广州510006【正文语种】中文【中图分类】TP391.41引言随着激光机器视觉技术在工业生产中的迅速发展，在3维自然纹理［1-2］立体印刷方面，物体表面3维轮廓测量技术应用广泛。
置信度边缘检测在眼底相机自动对焦中的应用
ａｉｎ ｆ ｎ ｔ ｎ ａ ｄ ｃ ｍｂｎ ｎ ｈ ｏ ｇ ｅ ｒ ｈ ｗ ｔ ｈ ｎ ｅ ｒ ｈ ｈ ｏ ｇ ｈ ｄ ｅ ｃ ｎｉ ｅ ｃ ｅｅ ｔ ｎ， ｈ ｅｉ ａ ｔ ｕ ｃｉ ｎ ｏ ｉ ｉｇ ｔ ｅ ｒ ｕ ｈ ｓ ａ ｃ ｉ ｔ ｅ ｆ ｅ ｓ ａ ｃ ．Ｔ ｒ ｕ ｈ ｔ ｅ ｅ ｇ ｏ ｆｄ ｎ ｅ ｄ ｔｃ ｉ ｏ ｏ ｈ ｉ ｏ ｔ ｅ ｒｔｎ ｌ
Ａ ＢＳ ＴＲ ＡＣＴ ： ｏ－ ｏ ｕ ｉｇ ｔｃ ｎｉｕｅ ｉ ｎ ｉ Ａｕｔ ｆ ｃ ｓｎ ｅ ｈ ｑ ｓ ａ ｍｐｏｔ ｎ ｔ ｄ ｔ ｍｐ ｏ ｅ ｔ ｅ ａ ｏ ｔｏ ｏ ｕ ｕｓｃ ｍｅ ａ． Ｉ ｓ ａ ｒａ ｔｍｅｈｏ ｏ ｉ ｒ ｖ ｈ ｕｔｍａ ｉｎ ｆｒｆ ｎｄ ａ ｒ ｔｉ ｋ ｙ ｔ ｕｔ－ｆ ｃ ｓｎ ｅ ｈｎｑｕ ｈａ ｅｅ ｔｎ ｍ ａ ｅ ｓ ａｐｎｅ ｓｅ ａｕａｉｎ ｆ ｎｃｉｎ． Ｉ ｒ ｅ ｏｅ ｕ ｅ ｔ ｅｆ ｃ ｓｎ ｃ ｅ ｏ ａ ｏ ｏ ｕ ｉｇ ｔ ｃ ｉ ｅ ｔ ｔｓ ｌ ｃｉ ｇ ｉ ｇ ｈ ｒ ｓ ｖ ｌ ｔｏ ｕ ｔ ｏ ｎ ｏ ｄ ｒｔ ｎｓ ｒ ｈ ｏ ｕ ｉｇ ａ － ｃ ａ ｙ，ｔ ｓｐａ ｅ ｏ ｏ ｅ ｅ ａ ｏ－ ｏ ｕ ｌ ｏｉｈｍ ， ｉ ｈ ｅ ｎ ｔ ｎ ｏ ｅ ｉ ｌｖ ｓ ｅ ｄ ｅ ａ ｈ ｒ ｎ ｓ ｖ ｌ ｕｒ ｃ ｈｉ ｐ ｒｐｒ ｐ ｓ ｓａ ｎ ｗ ｕｔ ｆ ｃ ｓａ ｇ ｒｔ ｕｓｎｇｔ ｅ ｄ ｆ ｉｉ ｆｒ ｔｎａ ｅ ｓ ｌｅ ｇ ｓｓ ａ ｉ ｏ ｐ ｅ ｓｅ ａｕ・
萨丕尔-沃尔夫假说
萨丕尔－沃尔夫假设主要内容美国人萨丕尔及其弟子沃尔夫提出的有关语言与思维关系的假设是这个领域里至今为止最具争议的理论。
沃尔夫首先提出，所有高层次的思维都倚赖于语言。
说得更明白一些，就是语言决定思维，这就是语言决定论这一强假设。
由于语言在很多方面都有不同，沃尔夫还认为，使用不同语言的人对世界的感受和体验也不同，也就是说与他们的语言背景有关，这就是语言相对论。
Linguistic relativity stems from a question about the relationship between language and thought, about whether one's language determines the way one thinks. This question has given birth to a wide array of research within a variety of different disciplines, especially anthropology, cognitive science, linguistics, and philosophy. Among the most popular and controversial theories in this area of scholarly work is the theory of linguistic relativity(also known as the Sapir–Whorf hypothesis). An often cited "strong version" of the claim, first given by Lenneberg in 1953 proposes that the structure of our language in some way determines the way we perceive the world. A weaker version of this claim posits that language structure influences the world view adopted by the speakers of a given language, but does not determine it.[1]由萨丕尔－沃尔夫假设的这种强假设可以得出这样的结论：根本没有真正的翻译，学习者也不可能学会另一种文化区的语言，除非他抛弃了他自己的思维模式，并习得说目的语的本族语者的思维模式。
样品检查中的图像对比度增强[发明专利]
![样品检查中的图像对比度增强[发明专利]](https://img.taocdn.com/s3/m/f6597f23bb4cf7ec4bfed057.png)
专利名称：样品检查中的图像对比度增强专利类型：发明专利
发明人：王義向,张楠
申请号：CN201880062821.4
申请日：20180925
公开号：CN111433881A
公开日：
20200717
专利内容由知识产权出版社提供
摘要：本文中公开了一种方法，包括：在第一时间段期间，将第一数量的电荷沉积到样品的区域中；在第二时间段期间，将第二数量的电荷沉积到该区域中；在扫描带电粒子的束在样品上生成的探针斑点的同时，从探针斑点中记录表示带电粒子的束与样品的相互作用的信号；其中第一时间段期间的平均沉积速率和第二时间段期间的平均沉积速率不同。
申请人：ASML荷兰有限公司
地址：荷兰维德霍温
国籍：NL
代理机构：北京市金杜律师事务所
代理人：傅远
更多信息请下载全文后查看。
计算机视觉常用术语中英文对照
---------------------------------------------------------------最新资料推荐------------------------------------------------------ 计算机视觉常用术语中英文对照计算机视觉常用术语中英文对照（1）人工智能 Artificial Intelligence 认知科学与神经科学Cognitive Science and Neuroscience 图像处理Image Processing 计算机图形学Computer graphics 模式识别 Pattern Recognized 图像表示 Image Representation 立体视觉与三维重建Stereo Vision and 3D Reconstruction 物体（目标）识别 Object Recognition 运动检测与跟踪Motion Detection and Tracking 边缘edge 边缘检测detection 区域region 图像分割segmentation 轮廓与剪影contour and silhouette1/ 10纹理 texture 纹理特征提取 feature extraction 颜色 color 局部特征 local features or blob 尺度 scale 摄像机标定 Camera Calibration 立体匹配stereo matching 图像配准Image Registration 特征匹配features matching 物体识别Object Recognition 人工标注Ground-truth 自动标注Automatic Annotation 运动检测与跟踪 Motion Detection and Tracking 背景剪除Background Subtraction 背景模型与更新background modeling and update---------------------------------------------------------------最新资料推荐------------------------------------------------------ 运动跟踪 Motion Tracking 多目标跟踪 multi-target tracking 颜色空间 color space 色调 Hue 色饱和度 Saturation 明度 Value 颜色不变性 Color Constancy（人类视觉具有颜色不变性）照明illumination 反射模型Reflectance Model 明暗分析Shading Analysis 成像几何学与成像物理学 Imaging Geometry and Physics 全像摄像机 Omnidirectional Camera 激光扫描仪 Laser Scanner 透视投影Perspective projection 正交投影Orthopedic projection3/ 10表面方向半球 Hemisphere of Directions 立体角 solid angle 透视缩小效应 foreshortening 辐射度 radiance 辐照度 irradiance 亮度 intensity 漫反射表面、Lambertian（朗伯）表面 diffuse surface 镜面 Specular Surfaces 漫反射率 diffuse reflectance 明暗模型 Shading Models 环境光照 ambient illumination 互反射interreflection 反射图Reflectance Map 纹理分析Texture Analysis 元素 elements---------------------------------------------------------------最新资料推荐------------------------------------------------------ 基元 primitives 纹理分类 texture classification 从纹理中恢复图像 shape from texture 纹理合成 synthetic 图形绘制 graph rendering 图像压缩 image compression 统计方法 statistical methods 结构方法 structural methods 基于模型的方法 model based methods 分形fractal 自相关性函数autocorrelation function 熵entropy 能量energy 对比度contrast 均匀度homogeneity5/ 10相关性 correlation 上下文约束 contextual constraints Gibbs 随机场吉布斯随机场边缘检测、跟踪、连接 Detection、Tracking、Linking LoG 边缘检测算法（墨西哥草帽算子）LoG=Laplacian of Gaussian 霍夫变化 Hough Transform 链码 chain code B-样条B-spline 有理 B-样条 Rational B-spline 非均匀有理 B-样条Non-Uniform Rational B-Spline 控制点control points 节点knot points 基函数 basis function 控制点权值 weights 曲线拟合 curve fitting---------------------------------------------------------------最新资料推荐------------------------------------------------------ 内插 interpolation 逼近 approximation 回归 Regression 主动轮廓Active Contour Model or Snake 图像二值化Image thresholding 连通成分connected component 数学形态学mathematical morphology 结构元structuring elements 膨胀Dilation 腐蚀 Erosion 开运算 opening 闭运算 closing 聚类clustering 分裂合并方法 split-and-merge 区域邻接图 region adjacency graphs7/ 10四叉树quad tree 区域生长Region Growing 过分割over-segmentation 分水岭watered 金字塔pyramid 亚采样sub-sampling 尺度空间 Scale Space 局部特征 Local Features 背景混淆clutter 遮挡occlusion 角点corners 强纹理区域strongly textured areas 二阶矩阵 Second moment matrix 视觉词袋 bag-of-visual-words 类内差异 intra-class variability---------------------------------------------------------------最新资料推荐------------------------------------------------------ 类间相似性inter-class similarity 生成学习Generative learning 判别学习discriminative learning 人脸检测Face detection 弱分类器weak learners 集成分类器ensemble classifier 被动测距传感passive sensing 多视点Multiple Views 稠密深度图 dense depth 稀疏深度图 sparse depth 视差disparity 外极epipolar 外极几何Epipolor Geometry 校正Rectification 归一化相关 NCC Normalized Cross Correlation9/ 10平方差的和 SSD Sum of Squared Differences 绝对值差的和 SAD Sum of Absolute Difference 俯仰角 pitch 偏航角 yaw 扭转角twist 高斯混合模型Gaussian Mixture Model 运动场motion field 光流 optical flow 贝叶斯跟踪 Bayesian tracking 粒子滤波 Particle Filters 颜色直方图 color histogram 尺度不变特征转换 SIFT scale invariant feature transform 孔径问题 Aperture problem。
contrastive denoising讲解 -回复
contrastive denoising讲解-回复什么是对抗式降噪（Contrastive Denoising）？对抗式降噪（Contrastive Denoising）是一种用于降低深度学习模型中的噪声的技术。
在深度学习中，噪声是指训练数据中的不完美或混乱，可能导致模型学到错误的模式或产生过拟合。
对抗式降噪的目标是通过将真实样本与人工引入的噪声样本进行对比，使模型学习有效的特征表示，并迫使模型从中选择出真实样本。
对抗式降噪的原理是通过引入经过特定改变的数据样本，即噪声样本，来影响模型的训练。
这些噪声样本经过一系列的操作，如加噪、旋转、裁剪等，使其与真实样本略有不同，但又足够接近。
然后，模型在训练过程中被要求区分真实样本和噪声样本，从而迫使模型学习到更加鲁棒和有效的特征表达。
对抗式降噪的步骤如下：1. 数据准备：首先，需要准备一批真实样本作为训练数据，这些真实样本应该是带有标签的，用于监督模型的学习过程。
同时，还需要生成一批噪声样本，这些噪声样本应该与真实样本具有相似的特征，但又有足够的差异用于区分。
2. 加噪处理：在准备噪声样本时，可以采用多种方式对真实样本进行加噪处理。
例如，可以通过向图像中添加高斯噪声、椒盐噪声或者对抗性攻击等方式来引入噪声。
噪声的引入应该在一定程度上保证真实样本与噪声样本之间的相似度。
3. 模型训练：将真实样本和噪声样本输入到深度学习模型中进行训练。
在训练过程中，模型需要学习到将真实样本和噪声样本区分开的能力。
这可以通过训练一个对抗模型来实现，该对抗模型由两部分组成：生成器和判别器。
4. 生成器和判别器：生成器负责生成噪声样本，而判别器则负责区分真实样本和噪声样本。
在训练过程中，生成器和判别器相互对抗，生成器的目标是生成越来越接近真实样本的噪声样本，而判别器的目标是尽可能准确地区分真实样本和噪声样本。
5. 损失函数：在对抗式降噪中，通常使用交叉熵损失作为训练模型的目标函数。
DC-CBAM-UNet++网络的肺结节图像分割方法
第 22卷第 7期2023年 7月Vol.22 No.7Jul.2023软件导刊Software GuideDC-CBAM-UNet++网络的肺结节图像分割方法徐微，汤俊伟，张驰（1.湖北省服装信息化工程技术研究中心； 2.武汉纺织大学计算机与人工智能学院，湖北武汉 430200）摘要：针对肺结节图像存在体积较小、形状不规则、边缘模糊，导致模型特征提取困难及分割精度不高等问题，提出一种基于UNet++结合空洞卷积与注意力机制的肺结节分割方法（DC-CBAM-UNet++）。
该方法在传统UNet++网络基础上引入空洞卷积（DC-UNet++），并增加注意力机制加强特征图获得更多加权占比，使特征图获得更大的感受野。
在LIDC肺结节公开数据集上的训练与验证结果表明，所提模型精确率、相似系数和交并比分别达到94.98%、90.86%、84.54%，证明了该方法的有效性，为分割肺结节图像提供了一种新方法。
关键词：UNet++；空洞卷积；注意力机制；图像分割DOI：10.11907/rjdk.221582开放科学（资源服务）标识码（OSID）：中图分类号：TP391 文献标识码：A文章编号：1672-7800（2023）007-0125-06Lung Nodule Image Segmentation Method Based on DC-CBAM-UNet++ NetworkXU Wei， TANG Junwei， ZHANG Chi（1.Engineering Research Center of Hubei Province for Clothing Information；2.School of Computer Science and Artificial Intelligence ， Wuhan Textile University ，Wuhan 430200， China）Abstract：To address the problems of small volume， irregular shape and blurred edges in lung nodule images， which lead to difficulty in fea‐ture extraction and low segmentation accuracy， propose a lung nodule segmentation method （DC-CBAM-UNet++） based on UNet++ com‐bined with cavity convolution and attention mechanism. In order to obtain a larger sense field for the feature map， this method improves the tra‐ditional UNet++ network ， introducing the null convolution （DC-UNet++） on the original basis， and also introducing the attention mechanism to enhance the feature map to obtain more weighted occupancy. Experiments were conducted using the LIDC lung nodule public dataset for training and validation， and the accuracy， similarity coefficient and cross-merge ratio reached 94.98%，90.86% and 84.54%， respectively，demonstrating the effectiveness of the method and providing a new method for segmenting pulmonary nodule images.Key Words：UNet++； dilated convolution； attention mechanism； image segmentation0 引言肺癌是一种严重疾病，死亡率较高，但病因尚不明晰，可能由于长期吸烟和所处的环境引起。
对比敏感度
角膜接触镜验配中的应用
3.糖尿病评估
糖尿病引起糖尿病性视网膜病变
早期发现视力仍正常的视觉功能异常
敏感的发现视力检查和眼底检查不能发现
的眼底病变
两眼对比敏感度不对称是糖尿病进展的早 期指标
男，33岁 视力1.0，眼部检查未见异常
两眼对比敏感度不对称 是糖尿病进展的早期指标
4.青光眼
Ct L max L min/ L max L min
对比敏感度=1/对比度阈值
Cs 1 / Ct
对比敏感度测定原理 3.对比度和对比敏感度
对比敏感度曲线
空间频率：横坐标 对比敏感度：纵坐标
对比敏感度测定原理 4.对比敏感度函数和限制因素
对比敏感度函数（Contrast Sensitivity Function， CSF） 整个视觉系统的综合：包括眼部、传导通 路和中枢 限制因素：眼部异常、传导异常、中枢异 常
对比敏感度在青光眼中的应用
治疗前评估：正常范围下、双眼不对称、 CS切迹，评估程度和进展 治疗效果：治疗前后（药物和手术）效果 评估
青光眼的主要临床体征
眼压升高、视野缺损、视乳头凹陷和视神经萎缩
5.准分子激光手术
5.准分子激光手术
准分子激光引起的CS变化： 手术对组织的创伤、一般数月后可恢复正 常 高度近视者术后对比敏感度可提高 切削直径和瞳孔大小可影响对比敏感度
CS在白内障治疗中的应用
术前0.5以上时，让然诉看不清 术后0.8以上，但仍然诉看不清 如何解决对比敏感度差 ：人工晶体、手术方式、术后光学矫正 （框架、角膜接触镜等）
基于灰度和直方图的阈值自适应镜头边界检测
基于灰度和直方图的阈值自适应镜头边界检测
黄茜;张海泉;杨文亮;吴元
【期刊名称】《科学技术与工程》
【年(卷),期】2008(008)014
【摘要】镜头是对视频内容进行分析和检索的基础,研究镜头边界检测具有重要的现实意义.在对视频以及视频镜头边界检测的现有方法分析后,提出了一种改进的镜头边界检测算法--基于灰度和直方图的阈值自适应镜头边界检测算法.该算法是在应用自适应阈值的基础上,结合灰度图和直方图的特征总结出来的.实验结果表明这种改进的算法具有很好的突变镜头边界检测效果,同时该算法还对不同性质的视频有很好的适用性.
【总页数】6页(P3787-3792)
【作者】黄茜;张海泉;杨文亮;吴元
【作者单位】华南理工大学电子与电信学院,广州,510640;华南理工大学电子与电信学院,广州,510640;华南理工大学电子与电信学院,广州,510640;School of Informatics, University of Bradford, Bradford BD7 1DP UK
【正文语种】中文
【中图分类】TP301.6
【相关文献】
1.基于因果的自适应双阈值镜头边界检测算法 [J], 孙少卿;卓力;赵士伟;张菁
2.基于直方图的微视频镜头边界检测方法的研究 [J], 黄菊
3.基于累积帧的自适应双阈值镜头边界检测算法 [J], 邓丽;金立左;杨文强;费敏锐
4.基于融合特征的自适应阈值镜头边界检测算法 [J], 李秋玲; 赵磊; 邵宝民; 王雷; 姜雪
5.双因子自适应阈值的镜头边界检测算法 [J], 方之昕;孙锬锋;蒋兴浩
因版权原因，仅展示原文概要，查看原文内容请购买。