Computer Vision: Algorithms and Application书籍章节介绍及其源码
ComputerVision:
Algorithms and Applications
Richard Szeliski
本书网址: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http://szeliski.org/Book
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
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。
Middlebury test datasets forevaluating MRF minimization/inference algorithms评估隐马尔科夫随机场最小化和推断算法,
http://vision.middlebury.edu/MRF/results/ (Szeliski, Zabih, Scharstein et al. 2008).
(Miko-lajczyk and Schmid 2005;Mikolajczyk, Tuytelaars, Schmid et al. 2005).
http://cvlab.epfl.ch/~brown/patchdata/patchdata.html
(Winder and Brown 2007;Hua,Brown, and Winder 2007).
Weizmann segmentationevaluation database of 100 grayscale images with ground truth segmentations,
http://www.wisdom.weizmann.ac.il/~vision/Seg EvaluationDB/index.html
(Alpert, Galun, Basri et al. 2007).
TheMiddlebury optic flow evaluation(光流评估) Web site, http://vision.middlebury.edu/flow/data/
(Baker,Scharstein, Lewis et al. 2009).
The Human-Assisted MotionAnnotation database,(人类辅助运动数据库)
http://people.csail.mit.edu/celiu/motionAnnotation/ (Liu, Freeman, Adelson etal. 2008)
High DynamicRange radiance(辐射)maps, http://www.debevec.org/Research/HDR/
Alpha matting evaluation Website, http://alphamatting.com/ (Rhemann, Rother, Wang
第十一章:Stereo correspondence立体对应
Middlebury Stereo Datasets andEvaluation, http://vision.middlebury.edu/stereo/ (Scharstein
toccia, Di Stefano et al.2008).
Middlebury Multi-View StereoDatasets,
http://vision.middlebury.edu/mview/data/ (Seitz,Curless, Diebel etal. 2006).
Multi-view and Oxford Collegesbuilding reconstructions,
http://www.robots.ox.ac.uk/~vgg/data/data-mview.html .
Multi-View Stereo Datasets, http://cvlab.epfl.ch/data/strechamvs/ (Strecha, Fransens,
Multi-View Evaluation, http://cvlab.epfl.ch/~strecha/multiview/ (Strecha, von Hansen,
The (New) Stanford Light FieldArchive, http://lightfield.stanford.edu/
(Wilburn, Joshi,Vaish et al.2005).
Virtual Viewpoint Video:multi-viewpoint video with per-frame depth maps,
http://research.microsoft.com/en-us/um/redmond/groups/ivm/vvv/ (Zitnick, Kang, Uytten-
查找一系列的视觉识别数据库,在表14.1–14.2.除了那些,这里还有:
Buffy pose classes, http://www.robots.ox.ac.uk/~vgg/data/ buffy pose classes/ andBuffy
stickmen 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/jointannotated photographs of humans,
http://www.eecs.berkeley.edu/~lbourdev/h3d/ (Bourdev and Malik 2009).
Action Recognition Datasets,http://www.cs.berkeley.edu/projects/vision/action, has point-
特征检测 (Canny, Harris, Hough, MSER, SURF);
运动分析和物体分析 (Lucas–Kanade, mean shift);
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, http://software.intel.com/en-us/intel-ipp/,包含
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, http://vxl.sourceforge.net/)
LTI-Lib 2 (http://www.ie.itcr.ac.cr/palvarado/ltilib-2/homepage/ ).
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:
matlabPyrTools—MATLAB 下的源码对于拉普拉斯变换,金字塔, QMF/小波, 和
steerable pyramids, http://www.cns.nyu.edu/~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-
C++ implementation of the fastdistance transform algorithm,
http://people.cs.uchicago.edu/~pff/dt/ (Felzenszwalb andHuttenlocher 2004a).
http://vlfeat.org/ (Vedaldi and Fulkerson 2008).
SiftGPU: A GPU Implementationof Scale Invariant Feature Transform (SIFT),
http://www.cs.unc.edu/~ccwu/siftgpu/ (Wu 2010).
SURF: Speeded Up RobustFeatures, http://www.vision.ee.ethz.ch/~surf/
(Bay, Tuyte-laars, and VanGool 2006).
FAST corner detection, http://mi.eng.cam.ac.uk/~er258/work/fast.html
(Rosten and Drum-mond 2005, 2006).
Linux binaries for affineregion detectors and descriptors, as well as MATLAB files to
compute repeatability andmatching scores,
http://www.robots.ox.ac.uk/~vgg/research/affine/
高效的基于图形的分割http://people.cs.uchicago.edu/~pff/segment
(Felzenszwalb and Huttenlocher2004b).
http://coewww.rutgers.edu/riul/research/code/EDISON/
(Meer and Georgescu 2001; Comaniciu and Meer2002).
Normalized cuts segmentationincluding intervening contours,
http://www.cis.upenn.edu/~jshi/software/
(Shi and Malik 2000; Malik,Belongie, Leung et al. 2001).
Segmentation by weightedaggregation (SWA),利用加权集合的分割
http://www.cs.weizmann.ac.il/~vision/SWA (Alpert, Galun, Basri et al.2007).
Non-iterative PnP algorithm,(非迭代PnP算法)
http://cvlab.epfl.ch/software/EPnP (Moreno-Noguer, Lep-etit, and Fua 2007).
Tsai Camera Calibration(相机矫正) Software,
http://www-2.cs.cmu.edu/~rgw/TsaiCode.html (Tsai 1987).
Camera Calibration Toolbox forMATLAB,
http://www.vision.caltech.edu/bouguetj/calib doc/ ; a C version is included in OpenCV.
MATLAB functions for multipleview geometry,
http://www.robots.ox.ac.uk/~vgg/hzbook/code/ (Hartley and Zisserman2004).
SBA: A generic sparse bundle(稀疏束) adjustment C/C++ package basedon the Levenberg–
Marquardt algorithm, http://www.ics.forth.gr/~lourakis/sba/ (Lourakis and Argyros 2009).
Simple sparse bundleadjustment (SSBA), http://cs.unc.edu/~cmzach/opensource.html .
Bundler, structure from motionfor unordered image collections(无序图像集),
http://phototour.cs.washington.edu/bundler/ (Snavely, Seitz, and Szeliski 2006).
光流, http://www.cs.brown.edu/~black/code.html (Black and Anan-
TV-L1 optical flow on the GPU, http://cs.unc.edu/~cmzach/opensource.html
(Zach,Pock, and Bischof2007a).
(Glocker, Komodakis, Tziritas et al. 2008).
Microsoft Research ImageCompositing Editor for stitching images,(图像拼接,图像合成)
http://research.microsoft.com/en-us/um/redmond/groups/ivm/ice/ .
http://www.robots.ox.ac.uk/~vgg/software/SR/ (Pickup 2007;Pickup, Capel,Roberts et al. 2007, 2009).
StereoMatcher, standalone C++stereo matching code,
http://vision.middlebury.edu/stereo/code/ (Scharstein and Szeliski2002).
Patch-based multi-view stereosoftware (PMVS Version 2),
http://grail.cs.washington.edu/software/pmvs/ (Furukawa and Ponce 2011).
Scanalyze: a system foraligning and merging range data,
http://graphics.stanford.edu/software/scanalyze/ (Curless and Levoy 1996).
MeshLab: software forprocessing, editing, and visualizing unstructured 3D triangular
meshes, http://meshlab.sourceforge.net/.
VRML viewers (various) arealso a good way to visualize texture-mapped 3D models.
节 12.6.4: Whole body modeling andtracking(全身建模和追踪)
HumanEva: baseline code forthe tracking of articulated human motion,
http://vision.cs.brown.edu/humaneva/ (Sigal, Balan, and Black 2010).
节 14.1.1: Face detection(人脸检测)
Sample face detection code andevaluation tools,
http://vision.ai.uiuc.edu/mhyang/face-detection-survey.html.
节 14.1.2: Pedestrian detection(行人追踪)
A simple object detector withboosting,
http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html
(Hastie, Tibshirani, and Friedman 2001;Torralba, Murphy, and Freeman 2007).
http://www.robots.ox.ac.uk/~vgg/software/UpperBody/ (Ferrari,Marin-Jimenez, andZisserman 2008).
2D articulated human poseestimation software,
节 14.2.2: Active appearance and 3Dshape models
AAMtools: An active appearancemodeling toolbox,
http://cvsp.cs.ntua.gr/software/AAMtools/ (Papandreou and Maragos2008).
FASTANN and FASTCLUSTER forapproximate k-means (AKM),
http://www.robots.ox.ac.uk/~vgg/software/ (Philbin, Chum, Isard et al. 2007).
Feature matching using fastapproximate nearest neighbors,
http://people.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN (Muja and Lowe 2009).
Two bag of words classifiers, http://people.csail.mit.edu/fergus/iccv2005/bagwords.html
(Fei-Fei and Perona 2005;Sivic, Russell, Efros et al. 2005).
A simple parts and structureobject detector,
http://people.csail.mit.edu/fergus/iccv2005/partsstructure.html
(Fischler and Elschlager 1973; Felzenszwalband Huttenlocher 2005).
节 14.5.1: Machine learning software
Support vector machines (SVM)software (
http://www.support-vector-machines.org/SVM soft.html )
SVMlight http://svmlight.joachims.org/ ;
LIBSVM, http://www.csie.ntu.edu.tw/~cjlin/libsvm/(Fan, Chen,and Lin 2005);
LIBLINEAR, http://www.csie.ntu.edu.tw/~cjlin/liblinear/ (Fan,Chang, Hsieh et al.2008).
Kernel Machines: links to SVM,Gaussian processes, boosting, and other machine
learning algorithms, http://www.kernel-machines.org/software .
Multiple kernels for imageclassification,
http://www.robots.ox.ac.uk/~vgg/software/MKL
(Varma and Ray 2007; Vedaldi, Gulshan, Varmaet al. 2009).
附录 A.1–A.2: Matrix decompositions(矩阵分解) and linear least squares(线性最小乘)
BLAS (BasicLinear Algebra Subprograms基本线性代数子程序),
http://www.netlib.org/blas/ (Blackford,Demmel, Dongarraet al. 2002).
LAPACK (Linear Algebra(线性代数) PACKage),
http://www.netlib.org/lapack/ (Anderson, Bai,Bischof etal. 1999).
GotoBLAS, http://www.tacc.utexas.edu/tacc-projects/.
ATLAS (Automatically TunedLinear Algebra Software),
http://math-atlas.sourceforge.net/ (Demmel, Dongarra, Eijkhoutet al. 2005).
Intel Math Kernel Library(MKL), http://software.intel.com/en-us/intel-mkl/.
http://developer.amd.com/cpu/Libraries/acml/Pages/default.aspx .
Robust PCA code(鲁棒主成分分析), http://www.salle.url.edu/~ftorre/papers/rpca2.html
Appendix A.3: Non-linear leastsquares非线性最小二乘
MINPACK, http://www.netlib.org/minpack/.
levmar: Levenberg–Marquardtnonlinear least squares algorithms, 非线性最小二乘
http://www.ics.forth.gr/~lourakis/levmar/ (Madsen, Nielsen, andTingleff 2004).
附录 A.4–A.5: Direct(直接) and iterative(迭代) sparse matrix(稀疏矩阵) solvers
PARDISO (iterative and sparsedirect solution), http://www.pardiso-project.org/.
TAUCS (sparse direct,iterative, out of core, preconditioners),
http://www.tau.ac.il/~stoledo/taucs/ .
HSL Mathematical SoftwareLibrary, http://www.hsl.rl.ac.uk/index.html .
ITSOL,MIQR, and other sparsesolvers,
http://www-users.cs.umn.edu/~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)
C++ code for efficient beliefpropagation for early vision,
http://people.cs.uchicago.edu/~pff/bp/ (Felzenszwalb andHuttenlocher 2006).
FastPD MRF optimization(最优化) code,
算法 C.1 Calgorithm for Gaussian random noise generation, using the Box–Mullertransform.
return ((double)rand()) / ((double) RAND MAX);
void grand(double& g1, double& g2)
#define M_PI 3.14159265358979323846
double x1 = n1 + (n1 == 0); /* guardagainst log(0) */
double sqlogn1 = sqrt(-2.0 * log (x1));
double angl = (2.0 * M PI) * n2;
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,considerthe 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。
正如我在前言中提到的,我希望提供和书中材料相一致的PPT,直到这些全部准备好,你最好的方式去看我在华盛顿大学上课时的PPT,和一写相关课程中用到的教案。
UW 455:Undergraduate Computer Vision,
http://www.cs.washington.edu/education/courses/455/.
UW576:Graduate Computer Vision,
http://www.cs.washington.edu/education/courses/576.
StanfordCS233B: Introduction to Computer Vision,
http://vision.stanford.edu/teaching/cs223b/.
MIT6.869: Advances in Computer Vision,
http://people.csail.mit.edu/torralba/courses/6.869/6.869.computervision.htm.
Berkeley CS 280: Computer Vision, http://www.eecs.berkeley.edu/~trevor/CS280.html
UNC COMP776: Computer Vision, http://www.cs.unc.edu/~lazebnik/spring10.
Middlebury CS 453: Computer Vision,
http://www.cs.middlebury.edu/~schar/courses/cs453-s10/.
Related courses have also been taught onthe topic of Computational Photography, e.g.,
CMU 15-463: Computational Photography, http://graphics.cs.cmu.edu/courses/15-463/.
MIT 6.815/6.865: Advanced ComputationalPhotography,
http://stellar.mit.edu/S/course/6/sp09/6.815
Stanford CS 448A: Computational photographyon cell phones,
http://graphics.stanford.edu/courses/cs448a-10/.
SIGGRAPH courses on ComputationalPhotography,
http://web.media.mit.edu/~raskar/photo/.
原文地址:
http://blog.csdn.net/senlerliu/article/details/49822093
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