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在这里,我特别声明:本文章的源作者是 杨晓冬 (个人邮箱:[email protected])。原文的链接是http://www.iask.sina.com.cn/u/2252291285/ish。版权归 杨晓冬 朋友所有。
我非常感谢原作者辛勤地编写本文章,并愿意共享出来。我也希望转载本文的各位朋友,要注明原作者和出处,以尊重原作者!
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By xdyang(杨晓冬[email protected])
本文是对现有的图像处理和计算机视觉的经典书籍(后面会有推荐)的一个补充。一般的图像处理书籍都是介绍性的介绍某个方法,在每个领域内都会引用几十上百篇参考文献。有时候想深入研究这个领域的时候却发现文献太多,不知如何选择。但实际上在每个领域都有那么三五篇抑或更多是非读不可的经典文献。这些文献除了提出了很经典的算法,同时他们的Introduction和Related work也是对所在的领域很好的总结。读通了这几篇文献也就等于深入了解了这个领域,比单纯的看书收获要多很多。写本文的目的就是想把自己所了解到的各个领域的经典文章整理出来,不用迷失在参考文献的汪洋大海里。
B.图像分析:对图像的内容进行分析,提取有意义的特征,以便于后续的处理。处理的仍然是单幅图像。
C.计算机视觉:对图像分析得到的特征进行分析,提取场景的语义表示,让计算机具有人眼和人脑的能力。这时处理的是多幅图像或者序列图像,当然也包括部分单幅图像。
关于图像处理,图像分析和计算机视觉的划分并没有一个很统一的标准。一般的来说,图像处理的书籍总会或多或少的介绍一些图像分析和计算机视觉的知识,比如冈萨雷斯的数字图像处理。而计算机视觉的书籍基本上都会包括图像处理和图像分析,只是不会介绍的太详细。其实图像处理,图像分析和计算机视觉都可以纳入到计算机视觉的范畴:图像处理->低层视觉(low level vision),图像分析->中间层视觉(middle level vision),计算机视觉->高层视觉(high level vision)。这是一般的计算机视觉或者机器视觉的划分方法。在本文中,仍然按照传统的方法把这个领域划分为图像处理,图像分析和计算机视觉。
(2)OpenCV有一堆图像处理和计算机视觉的大牛在维护,bug在逐步减少,每个新的版本都会带来不同的惊喜。而且它已经或者逐步在移植到不懂的平台,并提供了对Python的很好的支持。
(3)在OpenCV上可以尝试各种最新以及成熟的技术,而不需要自己从头去写,比如人脸检测(Harr,LBP),DPM(Latent SVM),高斯背景模型,特征检测,聚类,hough变换等等 。而且它还支持各种机器学习方法(SVM,NN,KNN,决策树,Boosting等),使用起来很简单。
(4)文档内容丰富,并且给出了很多示例程序。当然也有一些地方文档描述不清楚,不过看看代码就很清楚了。
(5)完全开源。可以从中间提取出任何需要的算法。
(6)从学校出来后,除极少数会继续在学术圈里,大部分还是要进入工业界。现在在工 业界,c/c++仍是主流,很多公司都会优先考虑熟悉或者精通OpenCV的。事实上,在学术界,现在OpenCV也大有取代matlab之势。以前的demo或者source code,很多作者都愿意给出matlab版本的,然后别人再呼哧呼哧改成c版本的。现在作者干脆给出c/c++版本,或者自己集成到OpenCV中去,这样能快速提升自己的影响力。
如果想在图像处理和计算机视觉界有比较深入的研究,并且以后打算进入这个领域工作的话,建议把OpenCV作为自己的主攻方向。如果找工作的时候敢号称自己精通OpenCV的话,肯定可以找到一份满意的工作。
由于个人精力和视野的关系,有一些我未涉足过的领域不敢斗胆推荐,只是列出了一些引用率比较高的文章,比如摄像机标定和立体视觉。不过将来,由于工作或者其他原因,这些领域也会接触到,我会逐步增减这些领域的文章。尽管如此,仍然会有疏漏,忘见谅。同时文章的挑选也夹带了一些个人的喜好,比如我个人比较喜欢low level方向的,尤其是IJCV和PAMI上面的文章,因此这方面也稍微多点,希望不要引起您的反感。如果有什么意见或者建议,欢迎mail我。文章和资源我都会在我的csdn blog和sina ishare同步更新。此申明:这些论文的版权归作者及其出版商所有,请勿用于商业目的。
个人blog: http://blog.csdn.net/dcraw
新浪iask地址:http://iask.sina.com.cn/u/2252291285/ish?folderid=868438
本文的安排如下。第一部分是绪论。第二部分是图像处理中所需要用到的理论基础,主要是这个领域所涉及到的一些比较好的参考书籍。第三部分是计算机视觉中所涉及到的信号处理和模式识别文章。由于图像处理与图像分析太难区分了,第四部分集中讨论了它们。第五部分是计算机视觉部分。最后是小结。
信号与系统(第2版) Alan V.Oppenheim等著 刘树棠译
离散时间信号处理(第2版) A.V.奥本海姆等著 刘树棠译
数字信号处理:理论算法与实现 胡广书 (编者)
现代信号处理 张贤达著
统计信号处理基础:估计与检测理论 Steven M.Kay等著 罗鹏飞等译
自适应滤波器原理(第4版) Simon Haykin著 郑宝玉等译
信号处理的小波导引:稀疏方法(原书第3版) tephane Malla著, 戴道清等译
信息论基础(原书第2版) Thomas M.Cover等著 阮吉寿等译
Pattern Recognition and Machine Learning Bishop, Christopher M. Springer
模式识别(英文版)(第4版) 西奥多里德斯著
Pattern Classification (2nd Edition) Richard O. Duda等著
Statistical Pattern Recognition, 3rd Edition Andrew R. Webb等著
模式识别(第3版) 张学工著
图像处理,分析与机器视觉 第三版 Sonka等著 艾海舟等译
Image Processing, Analysis and Machine Vision
( 附:这本书是图像处理与计算机视觉里面比较全的一本书了,几乎涵盖了图像视觉领域的各个方面。中文版的个人感觉也还可以,值得一看。)
数字图像处理 第三版 冈萨雷斯等著
Digital Image Processing
(附:数字图像处理永远的经典,现在已经出到了第三版,相当给力。我的导师曾经说过,这本书写的很优美,对写英文论文也很有帮助,建议购买英文版的。)
计算机视觉:理论与算法 Richard Szeliski著
Computer Vision: Theory and Algorithm
(附:微软的Szeliski写的一本最新的计算机视觉著作。内容非常丰富,尤其包括了作者的研究兴趣,比如一般的书里面都没有的Image Stitching和Image Matting等。这也从另一个侧面说明这本书的通用性不如Sonka的那本。不过作者开放了这本书的电子版,可以有选择性的阅读。
http://szeliski.org/Book/
Multiple View Geometry in Computer Vision 第二版Harley等著
引用达一万多次的经典书籍了。第二版到处都有电子版的。第一版曾出过中文版的,后来绝版了。网上也可以找到中英文版的电子版。)
计算机视觉:一种现代方法 DA Forsyth等著
Computer Vision: A Modern Approach
MIT的经典教材。虽然已经过去十年了,还是值得一读。期待第二版
Machine vision: theory, algorithms, practicalities 第三版 Davies著
(附:为数不多的英国人写的书,偏向于工业应用。)
数字图像处理 第四版 Pratt著
Digital Image Processing
(附:写作风格独树一帜,也是图像处理领域很不错的一本书。网上也可以找到非常清晰的电子版。)
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[2004 IVC] Robust wide-baseline stereo from maximally stable extremal regions
[2011 PAMI] Are MSER Features Really Interesting
http://en.wikipedia.org/wiki/Singapore_Airlines_Flight_006
最后一篇文章也是Fua课题组的,作者给出的demo效果相当好。
[1998 PAMI] Example-based learning for view-based human face detection
[2003 IJCV] Learning the Statistics of People in Images and Video
[2011 PAMI] Learning to Detect a Salient Object
[2012 PAMI] A Real-Time Deformable Detector
[2003 PAMI] Kernel-based object tracking
[2007 PAMI] Tracking People by Learning Their Appearance
[2008 ACM] Object Tracking A Survey
[2008 PAMI] Segmentation and Tracking of Multiple Humans in Crowded Environments
[2011 PAMI] Hough Forests for Object Detection, Tracking, and Action Recognition
[2011 PAMI] Robust Object Tracking with Online Multiple Instance Learning
[2012 IJCV] PWP3D Real-Time Segmentation and Tracking of 3D Objects
[1992 IEEE] Historical review of OCR research and development
Video OCR A Survey and Practitioner's Guide
[1981 AI] Determine Optical Flow
[1994 IJCV] Performance of optical flow techniques
[1995 ACM] The Computation of Optical Flow
[2004 TR] Tutorial Computing 2D and 3D Optical Flow
[2005 BOOK] Optical Flow Estimation
[2008 ECCV] Learning Optical Flow
[2011 IJCV] A Database and Evaluation Methodology for Optical Flow
[1998 IJCV] CONDENSATION—Conditional Density Propagation for Visual Tracking
[2002 TSP] A tutorial on particle filters for online nonlinear non-Gaussian Bayesian tracking
[2002 TSP] Particle filters for positioning, navigation, and tracking
[2003 SPM] particle filter
[1999 CVIU] Visual analysis of human movement_ A survey
[2001 CVIU] A Survey of Computer Vision-Based Human Motion Capture
[2005 TIP] Image change detection algorithms a systematic survey
[2006 CVIU] a survey of avdances in vision based human motion capture
[2007 CVIU] Vision-based human motion analysis An overview
[2007 IJCV] Pedestrian Detection via Periodic Motion Analysis
[2007 PR] A survey of skin-color modeling and detection methods
[2010 IVC] A survey on vision-based human action recognition
[2012 PAMI] Pedestrian Detection An Evaluation of the State of the Art
[2001 IJCV] Modeling the Shape of the Scene A Holistic Representation of the Spatial Envelope
[2001 PAMI] Visual Word Ambiguity
[2007 PAMI] A Thousand Words in a Scene
[2010 PAMI] Evaluating Color Descriptors for Object and Scene Recognition
[2011 PAMI] CENTRIST A Visual Descriptor for Scene Categorization
[2003 PAMI] Detecting moving shadows-- algorithms and evaluation
[1993 PR] IMPROVED MOMENT INVARIANTS FOR SHAPE DISCRIMINATION
[1993 PR] Pattern Recognition by Affine Moment Invariants
[1996 PR] IMAGE RETRIEVAL USING COLOR AND SHAPE
[2001 SMI] Shape matching similarity measures and algorithms
[2002 PAMI] Shape matching and object recognition using shape contexts
[2004 PR] Review of shape representation and description techniques
[2006 PAMI] Integral Invariants for Shape Matching
[2008] A Survey of Shape Feature Extraction Techniques
[1999 ICCV] Object recognition from local scale-invariant features
[2000 IJCV] Evaluation of Interest Point Detectors
[2003 CVIU] Speeded-Up Robust Features (SURF)
[2004 CVPR] PCA-SIFT A More Distinctive Representation for Local Image Descriptors
[2004 IJCV] Distinctive Image Features from Scale-Invariant Keypoints
[2010 IJCV] Improving Bag-of-Features for Large Scale Image Search
[2011 PAMI] SIFTflow Dense Correspondence across Scenes and its Applications
[2002 PAMI] Simultaneous Localization and Map-Building Using Active Vision
[2007 PAMI] MonoSLAM Real-Time Single Camera SLAM
[1973] Textural features for image classification
[1979 ] Statistical and structural approaches to texture
[1996 PAMI] Texture features for browsing and retrieval of image data
[2002 PR] Brief review of invariant texture analysis methods
[2012 TIP] Color Local Texture Features for Color Face Recognition
[2009] Online learning of robust object detectors during unstable tracking
[2010 CVPR] P-N Learning Bootstrapping Binary Classifiers by Structural Constraints
[2010 ICIP] FACE-TLD TRACKING-LEARNING-DETECTION APPLIED TO FACES
[2012 PAMI] Tracking-Learning-Detection
[2000 CMU TR] A System for Video Surveillance and Monitoring
[2000 PAMI] W4-- real-time surveillance of people and their activities
[2008 MVA] The evolution of video surveillance an overview
[2001 CVPR] Rapid object detection using a boosted cascade of simple features
[2004 IJCV] Robust Real-time Face Detection
文章总数:372
2012年: 10
2011年: 20
2010年: 20
2009年: 14
2008年: 18
2007年: 13
2006年: 14
2005年: 9
2004年: 24
2003年: 22
2002年: 21
2001年: 21
2000年: 23
1999年: 10
1998年: 22
1997年: 8
1996年: 9
1995年: 9
1994年: 7
1993年: 5
1992年: 11
1991年: 5
1990年: 6
1980-1989: 22
1960-1979: 9