计算机视觉算法与应用的一些测试数据集和源码站点.doc

以下是computer vision：algorithm and application计算机视觉算法与应用这本书中附录里的关于计算机视觉的一些测试数据集和源码站点，我整理了下，加了点中文注解。Computer Vision:Algorithms and ApplicationsRichard Szeliski在本书的最好附录中，我总结了一些对学生，教授和研究者有用的附加材料。这本书的网址/Book包含了更新的数据集和软件，请同样访问他。C.1 数据集一个关键就是用富有挑战和典型的数据集来测试你算法的可靠性。当有背景或者他人的结果是可行的,这种测试可能甚至包含更多的信息(和质量更好)。经过这些年，大量的数据集已经被提出来用于测试和评估计算机视觉算法。许多这些数据集和软件被编入了计算机视觉的主页。一些更新的网址，像CVonline (http:/homepages.inf.ed.ac.uk/rbf/CVonline ), VisionBib.Com (/ ), and Computer Vision online (/ ), 有更多最新的数据集和软件。下面，我列出了一些用的最多的数据集，我将它们让章节排列以便它们联系更紧密。第二章：图像信息CUReT: Columbia-Utrecht 反射率和纹理数据库Reectance and Texture Database, /CAVE/software/curet/ (Dana, van Ginneken, Nayar et al. 1999).Middlebury Color Datasets:不同摄像机拍摄的图像，注册后用于研究不同的摄像机怎么改变色域和彩色registered color images taken by different cameras to study how they transform gamuts and colors, /color/data/ Chakrabarti, Scharstein, and Zickler 2009).第三章：图像处理Middlebury test datasets for evaluating MRF minimization/inference algorithms评估隐马尔科夫随机场最小化和推断算法, /MRF/results/ (Szeliski, Zabih, Scharstein et al. 2008).第四章：特征检测和匹配Afne Covariant Features database（反射协变的特征数据集） for evaluating feature detector and descriptor matching quality and repeatability（评估特征检测和描述匹配的质量和定位精度）, http:/www.robots.ox.ac.uk/vgg/research/affine/(Miko-lajczyk and Schmid 2005; Mikolajczyk, Tuytelaars, Schmid et al. 2005).Database of matched image patches for learning （图像斑块匹配学习数据库）and feature descriptor evaluation（特征描述评估数据库）,http:/cvlab.epfl.ch/brown/patchdata/patchdata.html (Winder and Brown 2007; Hua,Brown, and Winder 2007).第五章;分割Berkeley Segmentation Dataset（分割数据库） and Benchmark of 1000 images labeled by 30 humans,（30个人标记的1000副基准图像）along with an evaluation, /Research/Projects/CS/vision/grouping/segbench/ (Martin, Fowlkes, Tal et al. 2001).Weizmann segmentation evaluation database of 100 grayscale images with ground truth segmentations, http:/www.wisdom.weizmann.ac.il/vision/Seg Evaluation DB/index.html (Alpert, Galun, Basri et al. 2007).第八章：稠密运动估计The Middlebury optic ow evaluation（光流评估） Web site, /flow/data/ (Baker, Scharstein, Lewis et al. 2009).The Human-Assisted Motion Annotation database,（人类辅助运动数据库）/celiu/motionAnnotation/ (Liu, Freeman, Adelson et al. 2008)第十章：计算机摄像学High Dynamic Range radiance（辐射）maps, /Research/HDR/ (De-bevec and Malik 1997).Alpha matting evaluation Web site, / (Rhemann, Rother, Wanget al. 2009).第十一章：Stereo correspondence立体对应Middlebury Stereo Datasets and Evaluation, /stereo/ (Scharsteinand Szeliski 2002).Stereo Classication（立体分类） and Performance Evaluation（性能评估） of different aggregation（聚类） costs for stereo matching（立体匹配）, http:/www.vision.deis.unibo.it/spe/SPEHome.aspx (Tombari, Mat-toccia, Di Stefano et al. 2008).Middlebury Multi-View Stereo Datasets, /mview/data/ (Seitz,Curless, Diebel et al. 2006).Multi-view and Oxford Colleges building reconstructions, http:/www.robots.ox.ac.uk/vgg/data/data-mview.html .Multi-View Stereo Datasets, http:/cvlab.epfl.ch/data/strechamvs/ (Strecha, Fransens,and Van Gool 2006).Multi-View Evaluation, http:/cvlab.epfl.ch/strecha/multiview/ (Strecha, von Hansen,Van Gool et al. 2008).第十二章：3D重建HumanEva: synchronized video（同步视频） and motion capture （动作捕捉）dataset for evaluation of articulated human motion, /humaneva/ Sigal, Balan, and Black 2010).第十三章：图像渲染The (New) Stanford Light Field Archive, / (Wilburn, Joshi,Vaish et al. 2005).Virtual Viewpoint Video: multi-viewpoint video with per-frame depth maps, /en-us/um/redmond/groups/ivm/vvv/ (Zitnick, Kang, Uytten-daele et al. 2004).第十四章：识别查找一系列的视觉识别数据库，在表14.114.2.除了那些，这里还有：Buffy pose classes, http:/www.robots.ox.ac.uk/vgg/data/ buffy pose classes/ and Buffystickmen V2.1, http:/www.robots.ox.ac.uk/vgg/data/stickmen/index.html (Ferrari,Marin-Jimenez, and Zisserman 2009; Eichner and Ferrari 2009).H3D database of pose/joint annotated photographs of humans, /lbourdev/h3d/ (Bourdev and Malik 2009).Action Recognition Datasets, /projects/vision/action, has point-ers to several datasets for action and activity recognition, as well as some papers.（有一些关于人活动和运动的数据库和论文） The human action database at http:/www.nada.kth.se/cvap/actions/ 包含更多的行动序列。C.2 软件资源一个对于计算机视觉算法最好的资源就是开源视觉图像库（opencv）(/wiki/),他有在intel的Gary Bradski和他的同事开发，现在由Willow Garage (Bradsky and Kaehler 2008)维护和扩展。一部分可利用的函数在/documentation/cpp/中： 图像处理和变换 (滤波，形态学，金字塔); 图像几何学的变换 (旋转，改变大小);混合图像变换 (傅里叶变换，距离变换); 直方图; 分割 (分水岭, mean shift); 特征检测 (Canny, Harris, Hough, MSER, SURF); 运动分析和物体分析 (LucasKanade, mean shift); 相机矫正和3D重建 机器学习 (k nearest neighbors, 支持向量机, 决策树, boost-ing, 随机树, expectation-maximization, 和神经网络).Intel的Performance Primitives (IPP) library, /en-us/intel-ipp/，包含各种各样的图像处理任务的最佳优化代码，许多opencv中的例子利用了这个库，加入他安装了，程序运行得更快。依据功能，他和Opencv有很多相同的运算处理，并且加上了额外的库针对图像视频压缩，信号语音处理和矩阵代数。MTALAB中的Image Processing Toolbox图像处理工具，/products/image/，包含常规的处理，空域变换（旋转，改变大小），常规正交，图像分析和统计学（变边缘，哈弗变换），图像增强（自适应直方图均衡，中值滤波），图像恢复（去模糊），线性滤波（卷积），图像变换（傅里叶，离散余弦变换）和形态学操作（连通域和距离变换）两个比较旧的库，它们没有被发展，但是包含了一些的有用的常规操作：VXL (C+ Libraries for Computer Vision Research and Implemen-tation, /) LTI-Lib 2 (http:/www.ie.itcr.ac.cr/palvarado/ltilib-2/homepage/ ). 图像编辑和视图包，例如Windows Live Photo Gallery, iPhoto, Picasa,GIMP, 和 IrfanView，它们对执行这些处理非常有用：常规处理任务，格式转换，观测你的结果。它们同样可以用于对图像处理算法有趣的实现参考，例如色调调整和去噪。这里他也有一些软件包和基础框架对你建一个实时视频处理的DEMOS很有用，Vision on Tap(/ )提供一个可以实时处理你的网络摄像头的网页服务(Chiu and Raskar 2009）。Video-Man (VideoManager, /处理实时的基于视频的DEMOS和应用非常有用，你也可以用MATLAB中的imread直接从任何URl（例如网络摄像头）中读取视频。下面，我列出了一些额外的网络资源，让章节排列以便它们看起来联系更紧密：第三章:图像处理matlabPyrToolsMATLAB 下的源码对于拉普拉斯变换，金字塔, QMF/小波, 和steerable pyramids, /lcv/software.php (Simoncelli and Adel-son 1990a; Simoncelli, Freeman, Adelson et al. 1992).BLS-GSM 图像去噪, http:/decsai.ugr.es/javier/denoise/ (Portilla, Strela,Wain-wright et al. 2003).Fast bilateral ltering code（快速双边滤波）, /jiawen/#code (Chen, Paris, and Durand 2007).C+ implementation of the fast distance transform algorithm,/pff/dt/ (Felzenszwalb and Huttenlocher 2004a). GREYCs Magic Image Converter, including image restoration software using regularization and anisotropic diffusion, /gimp.shtml (Tschumperl e and Deriche 2005).第四章：图像特征检测和匹配VLFeat, 一个开放便捷的计算机视觉算法库 / (Vedaldi and Fulkerson 2008).SiftGPU: A GPU Implementation of Scale Invariant Feature Transform (SIFT),GPU实现的尺度特征性变换/ccwu/siftgpu/ (Wu 2010).SURF: Speeded Up Robust Features, http:/www.vision.ee.ethz.ch/surf/ (Bay, Tuyte-laars, and Van Gool 2006).FAST corner detection, http:/mi.eng.cam.ac.uk/er258/work/fast.html (Rosten and Drum-mond 2005, 2006).Linux binaries for afne region detectors and descriptors, as well as MATLAB les tocompute repeatability and matching scores, http:/www.robots.ox.ac.uk/vgg/research/affine/KanadeLucasTomasi feature trackers: KLT, /stb/klt/ (Shi and Tomasi 1994); GPU-KLT, /cmzach/opensource.html (Zach,Gallup, and Frahm 2008); LucasKanade 20 Years On, /projects/project 515.html (Baker and Matthews 2004).第五章：分割高效的基于图形的分割/pff/segment (Felzenszwalb and Huttenlocher 2004b).EDISON, 边缘检测和图像追踪, /riul/research/code/EDISON/ (Meer and Georgescu 2001; Comaniciu and Meer 2002).Normalized cuts segmentation including intervening contours, /jshi/software/ (Shi and Malik 2000; Malik, Belongie, Leung et al. 2001).Segmentation by weighted aggregation (SWA),利用加权集合的分割 http:/www.cs.weizmann.ac.il/vision/SWA (Alpert, Galun, Basri et al. 2007).第六章：基于特征的对齐和校准Non-iterative PnP algorithm,（非迭代PnP算法） http:/cvlab.ep.ch/software/EPnP (Moreno-Noguer, Lep-etit, and Fua 2007).Tsai Camera Calibration（相机矫正） Software, /rgw/TsaiCode.html (Tsai 1987).Easy Camera Calibration Toolkit,（简易相机校准工具包） /en-us/um/people/zhang/ Calib/ (Zhang 2000).Camera Calibration Toolbox for MATLAB, /bouguetj/calib doc/ ; a C version is included in OpenCV.MATLAB functions for multiple view geometry, http:/www.robots.ox.ac.uk/vgg/hzbook/code/ (Hartley and Zisserman 2004).第七章：运动重建SBA: A generic sparse bundle(稀疏束) adjustment C/C+ package based on the LevenbergMarquardt algorithm, http:/www.ics.forth.gr/lourakis/sba/ (Lourakis and Argyros 2009).Simple sparse bundle adjustment (SSBA), /cmzach/opensource.html .Bundler, structure from motion for unordered image collections(无序图像集), /bundler/ (Snavely, Seitz, and Szeliski 2006).第八章:稠密运动估计光流, /black/code.html (Black and Anan-dan 1996).Optical ow（光流） using total variation（全变量差） and conjugate gradient descent（共轭梯度下降）, /celiu/OpticalFlow/ (Liu 2009).TV-L1 optical ow on the GPU, /cmzach/opensource.html (Zach,Pock, and Bischof 2007a).elastix: a toolbox for rigid（刚性） and nonrigid（非刚性） registration of images（配准图像）, http:/elastix.isi.uu.nl/ (Klein, Staring, and Pluim 2007).Deformable image registration（可变形的配准图像） using discrete optimization（离散最优化）, /deformable/index.html (Glocker, Komodakis, Tziritas et al. 2008).第九章：图像缝合Microsoft Research Image Compositing Editor for stitching images,（图像拼接，图像合成） /en-us/um/redmond/groups/ivm/ice/ .第十章：计算机摄影学HDRShop software for combining bracketed exposures（包围式曝光） into high-dynamic range radiance images, /graphics/HDRShop/.Super-resolution（超分辨率） code, http:/www.robots.ox.ac.uk/vgg/software/SR/ (Pickup 2007;Pickup, Capel, Roberts et al. 2007, 2009).第十一章：立体对应StereoMatcher, standalone C+ stereo matching code, /stereo/code/ (Scharstein and Szeliski 2002).Patch-based multi-view stereo software (PMVS Version 2), /software/pmvs/ (Furukawa and Ponce 2011).第十二章：3D重建Scanalyze: a system for aligning and merging range data, /software/scanalyze/ (Curless and Levoy 1996).MeshLab: software for processing, editing, and visualizing unstructured 3D triangularmeshes, /.VRML viewers (various) are also a good way to visualize texture-mapped 3D models.节 12.6.4: Whole body modeling and tracking（全身建模和追踪）Bayesian 3D person tracking（贝叶斯3D人体追踪）, /black/code.html (Sidenbladh,Black, and Fleet 2000; Sidenbladh and Black 2003).HumanEva: baseline code for the tracking of articulated human motion, /humaneva/ (Sigal, Balan, and Black 2010).节 14.1.1: Face detection（人脸检测）Sample face detection code and evaluation tools,/mhyang/face-detection-survey.html.节 14.1.2: Pedestrian detection（行人追踪）A simple object detector with boosting, /torralba/shortCourseRLOC/boosting/boosting.html (Hastie, Tibshirani, and Friedman 2001; Torralba, Murphy, and Freeman 2007).Discriminatively（有区别） trained deformable（可变形） part models, /pff/latent/ (Felzenszwalb, Girshick, McAllester et al. 2010).Upper-body detector（上身检测）, http:/www.robots.ox.ac.uk/vgg/software/UpperBody/ (Ferrari,Marin-Jimenez, and Zisserman 2008).2D articulated human pose estimation software, http:/www.vision.ee.ethz.ch/calvin/articulated_human_pose_estimation_code/ (Eichner and Ferrari 2009).节 14.2.2: Active appearance and 3D shape modelsAAMtools: An active appearance modeling toolbox, http:/cvsp.cs.ntua.gr/software/AAMtools/ (Papandreou and Maragos 2008).节 14.3: Instance recognitionFASTANN and FASTCLUSTER for approximate k-means (AKM), http:/www.robots.ox.ac.uk/vgg/software/ (Philbin, Chum, Isard et al. 2007).Feature matching using fast approximate nearest neighbors, http:/people.cs.ubc.ca/mariusm/index.php/FLANN/FLANN (Muja and Lowe 2009).节 14.4.1: Bag of words(词袋)Two bag of words classiers, /fergus/iccv2005/bagwords.html (Fei-Fei and Perona 2005; Sivic, Russell, Efros et al. 2005).Bag of features and hierarchical（分层） k-means, / (Nist er and Stew enius2006; Nowak, Jurie, and Triggs 2006).节 14.4.2: Part-based modelsA simple parts and structure object detector, /fergus/iccv2005/partsstructure.html (Fischler and Elschlager 1973; Felzenszwalb and Huttenlocher 2005).节 14.5.1: Machine learning softwareSupport vector machines (SVM) software (/SVM soft.html ) 包含很多支持向量机的库, SVMlight / ; LIBSVM, .tw/cjlin/libsvm/ (Fan, Chen,and Lin 2005); LIBLINEAR, .tw/cjlin/liblinear/ (Fan,Chang, Hsieh et al. 2008).Kernel Machines: links to SVM, Gaussian processes, boosting, and other machinelearning algorithms, /software .Multiple kernels for image classication, http:/www.robots.ox.ac.uk/vgg/software/MKL (Varma and Ray 2007; Vedaldi, Gulshan, Varma et al. 2009).附录 A.1A.2: Matrix decompositions（矩阵分解） and linear least squares（线性最小乘）BLAS (Basic Linear Algebra Subprograms基本线性代数子程序), /blas/ (Blackford,Demmel, Dongarra et al. 2002).LAPACK (Linear Algebra（线性代数） PACKage), /lapack/ (Anderson, Bai,Bischof et al. 1999).GotoBLAS, /tacc-projects/.ATLAS (Automatically Tuned Linear Algebra Software), / (Demmel, Dongarra, Eijkhout et al. 2005).Intel Math Kernel Library (MKL), /en-us/intel-mkl/.AMD CoreMath Library (ACML), /cpu/Libraries/acml/Pages/default.aspx .Robust PCA code（鲁棒主成分分析）, /ftorre/papers/rpca2.html (De la Torre and Black 2003).Appendix A.3: Non-linear least squares非线性最小二乘MINPACK, /minpack/.levmar: LevenbergMarquardt nonlinear least squares algorithms, 非线性最小二乘 http:/www.ics.forth.gr/lourakis/levmar/ (Madsen, Nielsen, and Tingleff 2004).附录 A.4A.5: Direct（直接） and iterative（迭代） sparse matrix（稀疏矩阵） solversSuiteSparse (various reordering algorithms, 各种各样的重排算法CHOLMOD) and SuiteSparse QR, /research/sparse/SuiteSparse/ (Davis 2006, 2008).PARDISO (iterative and sparse direct solution), /.TAUCS (sparse direct, iterative, out of core, preconditioners), http:/www.tau.ac.il/stoledo/taucs/ .HSL Mathematical Software Library, http:/www.hsl.rl.ac.uk/index.html .Templates for the solution of linear systems（线性系统解决问题的模板）, /linalg/html templates/Templates.html (Barrett, Berry, Chan et al. 1994). Download the PDF for instructions（说明） on how to get the software.ITSOL,MIQR, and other sparse solvers, /saad/software/ (Saad 2003).ILUPACK, http:/www-public.tu-bs.de/bolle/ilupack/ .附录 B: Bayesian modeling and inference（贝叶斯建模和推断）Middlebury source code for MRF minimization（隐马尔科夫随机场最小化）, /MRF/code/ (Szeliski, Zabih, Scharstein et al. 2008).C+ code for efcient belief propagation for early vision, /pff/bp/ (Felzenszwalb and Huttenlocher 2006).FastPD MRF optimization（最优化） code, http:/www.csd.uoc.gr/komod/FastPD (Komodakisand Tziritas 2007a; Komodakis, Tziritas, and Paragios 2008)算法 C.1 C algorithm for Gaussian random noise generation, using the BoxMuller transform.C描述的利用BoxMuller 变换产生高斯随机噪声double urand()return (double) rand() / (double) RAND MAX);void grand(double& g1, double& g2)#ifndef M_PI#define M_PI 3.14159265358979323846#endif / M_PIdouble n1 = urand();double n2 = urand();double x1 = n1 + (n1 = 0); /* guard against log(0) */double sqlogn1 = sqrt(-2.0 * log (x1);double angl = (2.0 * M PI) * n2;g1 = sqlogn1 * cos(angl);g2 = sqlogn1 * sin(angl); 高斯噪声的产生。许多基本的软件包产生一些不同的随机的噪声(例如 运行在unix上的rand()，但是并不是所有的都有高斯随机噪声发生器。计算一个离散随机常量，你可以用BoxMuller transform (Box and Muller 1958)，他的c代码在算法C.1中给出了，注意这个运行结果是返回一对随机变量。相关的产生高斯随机变量的方由Thomas, Luk, Leong et al. (2007)提出。伪彩色产生。在很多应用中，很方便给图像加上标记（或者给图像特征比如线）。一个最简单的方式就是给不同的标记不同的颜色。在我的工作中，我发现用RGB立体色彩系给不同的标记赋予标准均匀的色彩是很方便的。对于每一个（非消极）标记值，consider the bits as being split among the three color channel，例如对于一个比特值为9的值，这个值可以被标记为RGBRGBRGB，获得三基色中的每一种颜色值后，颠倒比特值，结果是低位的比特值变化的最快。实际上，对于一个八比特的颜色通道，这个比特值的颠倒可以被存在一个表或者一个存储提前计算好的记录有由标记值向伪彩色的改变的完整表。图 8.16 显示了这样一个伪彩色绘制的例子.GPU实现GPU的出现，可以处理像素着色和计算着色，导致了实时应用的快速计算机视觉算法的发展，例如，分割，追踪，立体和运动估计（(Pock, Unger, Cremerset al. 2008; Vineet and Narayanan 2008; Zach, Gallup, an