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
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