图像处理经典文献

2015-12-03 08:43:26 GarfieldEr007 阅读数 2096

程序实例

声明:本资源仅供清华大学计算机系学生学习数字图象处理课程使用,未经允许不得用于其它目的。这里的程序都是清华大学计算机系人机交互与媒体实验室的教师与学生编制的,仅供参考,问题难免。

1.    位图(Bitmap)文件的数据解析 (程序设计:武勃)

功能:读入写出位图文件,分别提取RGB多通道格式的R,G,B分量,为图象处理提供基本程序框架。

2.    数字图象的基本处理(直方图均衡化、边缘检测、阈值处理、形态学处理等)(程序设计:梁路宏)

功能:读入多种格式的图象文件,具有各种基本处理功能,构成通用图象处理的程序框架。

3.    数字图象的基本处理(直方图均衡化、边缘检测、阈值处理、形态学处理、细化、FFT等) (程序设计:武勃)

4.    离散傅立叶变换和小波变换(程序设计:刘亚, 林凡,韩丹)

5.    Houph 变换(程序设计:张茺)

6.    工作台上平面工件的分割与识别(阈值处理、形态学处理、边界跟踪、Fourier描述子、形状识别等)(程序设计:艾海舟)

功能:将简单背景下的物体分割出来,并通过边缘跟踪获得其边界,再利用形状描述参数进行识别,构成工业条件下机器视觉应用的演示实例。

7.    面向视觉监视的变化检测(程序设计:艾海舟、吕风军)

功能:动态视频输入(如支持VFW标准视频或通过Matrox图象采集卡的视频信号),检测图象的变化部分,将其存储并提供浏览查询功能,构成一个简单的应用案例。

8.    基于模板匹配或差分图象的物体(如人脸)检测与跟踪(程序设计:吕风军、王栓、江潍)

功能:动态视频输入(如支持VFW标准视频或通过Matrox图象采集卡的视频信号),检测并跟踪工作台前电脑操作者,构成一个简单的演示实例。

9.    基于多高斯背景模型的运动物检测与跟踪(程序设计:刘亚)

10.基于主元分析(PCA)的人脸检测(程序设计:吕风军)

功能:检测输入图象中的人脸,构成一个利用统计分析方法的实例。

11.基于肤色分割与模板的人脸检测(程序设计:梁路宏、孙放)

12.夜间城市道路照明状况的测量与分析(Luminance measurement via video)(程序设计:艾海舟)

13.视觉监视中的运动目标检测与跟踪(Motion object detection and tracking for visual surveillance)(程序设计:艾海舟)

14.摄象机模型的校准(程序设计:艾海舟)

功能:建立针眼摄象机模型或双平面模型。

15.基于线条特征的立体视觉(程序设计:艾海舟)

功能:抽取立体对图象的线条特征,并建立对应,计算视差,恢复深度信息。

附注:上述程序主要是标准C或BC,MS-VC程序,只有PCA的计算部分是Matlab程序。虽然利用Matlab可以很方便地编写基本的图象处理程序,但本课程要求学生熟练地掌握用MS-VC开发工具编写基于Windows的基本图象处理应用程序,这是对计算机系学生的基本要求。此外,还要求同学结合Matlab提供的图象处理工具包设计需要比较复杂数学计算的图象处理程序。

联系人:艾海舟ahz@mail.tsinghualedu.cn


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清华大学计算机系 艾海舟

最近修改时间:2001年7月19日



参考文献:

1.      朱志刚,数字图象处理,清华大学计算机系,1998.7

2.     K. R.Castleman, Digital Image Processing, 清华大学出版社& Prentice Hall,1998.

3.     K. R.Castleman, (朱志刚、林学闫、石定机等译), 数字图象处理,电子工业出版社& PrenticeHall, 1998.

4.    Milan Sonka, Vaclav Hlavac, and Roger Boyle, Image processing,analysis, and machine vision , Chapman & Hall Computing, London, 1993. (网上消息:现在有更新的版本(第二版),2ndEdition, Brooks/Cole Publishing,1999.)

5.    Daisheng Luo, Pattern recognition and image processing, Chichester,Horwood Publishing, 1998

6.     崔屹,数字图象处理技术与应用,电子工业出版社,1997.3

7.    吕风军,数字图象处理编程入门,清华大学出版社,1999.9

8.    周长发,精通Visual C++图像编程,电子工业出版社,2000.1

清华大学图书馆的其它参考文献:

9.    章毓晋,图象工程 (上册) 图象处理和分析,清华大学出版社, 北京,1999

10.Edward Dougherty (ed.), Mathematical morphology in image processing,M. Dekker, New York, 1993.

11.崔屹,图象处理与分析---数学形态学方法与应用,科学出版社,北京

12.Theo Pavlidis, Algorithms for Graphics and Image Processing,Computer Science Press Inc., 11 Taft Court, Rockville, MD 20850, 1982.

13.Ferdinand van der Heijden, Image based measurement systems: objectrecognition and parameter estimation, John Wiley & Sons, Chichester, 1994.

14.Ning Lu, Fractal imaging, Academic Press, San Diego, 1997

15.Cornelius T. Leondes (ed), Image processing and pattern recognition,Calif. Academic press, San Diego, 1998

16.Sing-Tze Bow, Pattern recognition and image preprocessing, M.Dekker, New York, 1992

17.Rama Chellappa, Digital image processing, IEEE Computer SocietyPress, Los Alamitos, Calif., 1992.

18.William K. Pratt., Digital image processing, Wiley, New York, 1991.

19.Ioannis Pitas, Digital image processing algoriths, Prentice Hall,New York, 1993.

20.S.J. Sangwine and R.E.N. Horne (eds), The colour image processinghandbook, Chapman & Hall, London; New York, 1998

21.Linda Shapiro, Azriel Rosenfeld (eds), Computer vision and imageprocessing, Academic Press, Boston, 1992.

22.Bernd Jahne, Digital image processing : concepts, algorithms, andscientific applications, Springer-Verlag, New York, 1991.

23.Bernd Jahne, Spatio-temporal image processing : theory andscientific applications, Springer-Verlag, Berlin, 1993.

24.H.I. Christensen, J.L. Crowley, Experimental environments forcomputer vision and image processing, World Scientific, Singapore, 1994.

25.Anil K. Jain, Fundamentals of digital image processing, PrenticeHall, Englewood Cliffs, NJ, 1989.

26.Louis J. Galbiati, Machine vision and digital image processingfundamentals, Prentice Hall, Englewood Cliffs, N.J., 1990.

27.Jean Serra and Pierre Soille (eds), Mathematical morphology and itsapplications to image processing, Kluwer Academic Publishers, Boston, 1994.

28.Henk J.A.M. Heijmans and Jos B.T.M. Roerdink (eds), Mathematicalmorphology and its applications to image and signal processing, Kluwer,Dordrecht ; Boston, 1998.

29.M. Ibrahim Sezan, Reginald L. Lagendijk (eds), Motion analysis andimage sequence processing, Kluwer Academic Publishers, Boston, 1993.

30.Rhys Lewis, Practical digital image processing, Ellis Horwood, NewYork, 1990.

31.Tomasz Szoplik (ed), Selected papers on morphological imageprocessing: principles and optoelectronic implementations, SPIE OpticalEngineering Press, Bellingham, Wash., 1996

32.Jae S. Lim, Two-dimensional signal and image processing, PrenticeHall, Englewood Cliffs, N.J., 1990.

33.L. Prasad and S.S. Iyengar, Wavelet analysis with applications toimage processing, BCRC Press, oca Raton, 1997

34.Borko Furht, Stephen W. Smoliar, HongJiang Zhang, Video and imageprocessing in multimedia systems, Kluwer Academic Publishers, Boston ,1995.

35.Vasudev Bhaskaran, Konstantinos Konstantinides, Image and videocompression standards: algorithms and architectures, Kluwer AcademicPublishers, Boston, 1995.

36.G. Tziritas, C. Labit, Motion analysis for image sequence coding,Elsevier, New York, 1994.

37.Stephen Maybank, Theory of reconstruction from image motion,Springer-Verlag, Berlin, 1993.

38.Zhengyou Zhang, Olivier Faugeras, 3D dynamic scene analysis : astereo based approach, Springer-Verlag, Berlin, 1992.

39.Nicholas Ayache, Artificial vision for mobile robots : stereo visionand multisensory perception, MIT Press, Cambridge, Mass., 1991.

其它书籍:Pattern Recognition Books (Delft Pattern RecognitionGroup)

o       Image Processing

o       Pattern Recognition

o       Neural Networks

o       Books of Historical Interest

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Books onImage Processing and Vision

 H. Bassmann, P.W. Besslich, Digital Image Processing, Thomson, 1997.

 T. Lindeberg, Scale-space theory in computer vision, Kluwer AcademicPublishers, 1994

 V.S. Nalwa, A guided tour of computer vision, Addision-Wesley, 1993.

 Pitas, Digital image processing algorithms, Prentice-Hall, EnglewoodCliffs, 1993.

 Rafael C. Gonzalez and Richard E. Woods, Digital Imaging Processing,Addison-Wesley, Reading, Massachusetts, USA, 1992.

 R.M. Haralick and L.G. Shapiro, Computer and robot vision, volume I,Addison-Wesley, Reading, 1992.

 R.M. Haralick and L.G. Shapiro, Computer and robot vision, volumeII, Addison-Wesley, Reading, 1993.

 J.C. Russ, The Image Processing Handbook, CRC Press, Inc., BocaRaton, Ann Arbor, London, Tokyo, 1992.

 D. Vernon, Machine vision - Automated visual inspection and robotvision, Prentice Hall, New York, 1991. W.K. Pratt, Digital Image Processing(second edition), John Wiley & Sons, New York, 1991.

 L.J. Galbiati, Jr, Machine vision and digital image processingfundamentals, Prentice-Hall International, Inc, Englewood Cliffs, 1990.

 M. Ejiri, Machine vision - A practical technology for advanced imageprocessing, Gordon and Breach Science Publishers, New York, 1989.

 J.C. Simon, From pixels to features, North Holland, Amsterdam, 1989.

 Jan Teubner, Digital Image Processing, Prentice Hall, Copenhagen,1989.

 A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall,Englewood Cliffs, 1989.

 B.K.P. Horn, Robot Vision, MIT Press, Cambridge, 1987.

 M.J.B. Duff and T.J. Fountain, Cellular logic image processing, AcademicPress, London, 1986.

 John C. Russ, Practical Stereology, Plenum Press, New York, 1986.

 D.E. Dudgeon and R.M. Mersereau, Multidimensional digital signalprocessing, Prentice-Hall, Inc, Englewood Cliffs, 1984.

 Rosenfeld and A.C. Kak, Digital picture processing, volume 1,Academic Press, Orlando, 1982.

 Rosenfeld and A.C. Kak, Digital picture processing, volume 2,Academic Press, Orlando, 1982.

 J. Serra, Image analysis and mathematical morphology, AcademicPress, London, 1982.

 D.H. Ballard and C.M. Brown, Computer vision, Prentice-Hall,Englewood Cliffs, 1982.

 D. Marr, Vision, W.H. Freeman and Company, San Fransisco, 1982.

Books on PatternRecognition

 T.M. Mitchell, Machine learning, Mc Graw-Hill, New York, 1997.

 J. Schurmann, Pattern classification, a unified view of statisticaland neural approaches, John Wiley & Sons, New York, 1996.

 V.N. Vapnik, The Nature of Statistical Learning Theory,Springer,1996.

 B. Ripley, Pattern Recognition and Neural Networks, CambridgeUniversity Press, Cambridge, 1996.

 C.M. Bishop, Neural Networks for Pattern Recognition, ClarendonPress, Oxford, 1995.

 D. Paulus and J. Hornegger, Pattern Recognition and Image Processingin C++, Vieweg, Braunschweig, 1995.

 J.R. Quinlan, C4.5: Programs for machine learning, Morgan KaufmannPublishers, San Mateo, California, 1993.

 Robert Schalkhoff, Pattern Recognition, statistical, structural andneural approaches, John Wiley and Sons, New York, 1992.

 G.J. McLachlan, Discriminant Analysis and Statistical PatternRecognition, John Wiley and Sons, New York, 1992.

 S.M. Weiss and C.A. Kulikowski, Computer Systems that Learn, MorganKaufmann, San Mateo, California, 1991.

 K. Fukunaga, Introduction to Statistical Pattern Recognition (SecondEdition), Academic Press, New York, 1990.

 Y.H. Pao, Adaptive Pattern Recognition and Neural Networks, AddisonWesley, Reading, Massachusetts, 1989.

 Satoshi Watanabe, Pattern Recognition, Human and Mechanical, JohnWiley & Sons, New York, 1985.

 T.Y. Young and K.S. Fu, Handbook of Pattern Recognition and ImageProcessing, Academic Press, Orlando, Florida, 1986.

 L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone,Classification and regression trees, Wadsworth, California, 1984.

 P.A. Devijver and J. Kittler, Pattern Recognition, a StatisticalApproach, Prentice Hall, Englewood Cliffs, London, 1982.

 R.C. Gonzalez and M.G. Thomason, Syntactic pattern recognition - Anintroduction, Addison-Wesley, Reading, 1982.

 J. Sklanski and G.N. Wassel, Pattern Classifiers and TrainableMachines, Springer, New York, 1981.

 . R.O. Duda and P.E. Hart, Pattern classification and scene analysis,John Wiley & Sons, New York, 1973.

 (A second edition is being prepared by David Stork)

Books on NeuralNetworks

 T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995, 1997.

 LiMin Fu, Neural Networks in Computer Intelligence, McGraw-Hill,Inc., New York, NY, 1994.

 S. Haykin, Neural Networks, A Comprehensive Foundation, Macmillan,New York, NY, 1994.

 S.Y. Kung, Digital Neural Networks, Prentice Hall, Englewood Cliffs,NJ, 1993.

 Stephen I. Gallant, Neural Network Learning and Expert systems,Massachusetts Inst. of Technology, Cambridge, Massachusetts, 1993.

 Cichocki and R. Unbehauen, Neural Networks for Optimization andSignal Processing, John Wiley & Sons, New York, 1993.

 J.M. Zurada, Artificial Neural Systems, West Publishing, St. Paul,MN, 1992.

 Muller and J. Reinhardt, Neural networks, an introduction,Springer-Verlag, Berlin, 1991.

 P.D. Wasserman, Neural Computing, theory and practice, Van NostrandReinhold, New York, 1989.

 John Hertz, Anders Krogh, and Richard G. Palmer, Introduction to theTheory of Neural Computation, Addison Wesley Publ. Comp., Redwood City ,CA.

 Aleksander, Neural Computing Architectures, North Oxford Academic,London, 1989.

 S. Grossberg, The Adaptive Brain I: Cognition, Learning,Reinforcement, and Rythm, Elsevier/North Holland, Amsterdam, 1987.

 S. Grossberg, The Adaptive Brain II: Vision, Speech, Language andMotor Control, Elsevier/North Holland, Amsterdam, 1987.

Books of Historical Interest

 K. Fukunaga, Introduction to Statistical Pattern Recognition (FirstEdition), Academic Press, New York, 1972.

 J.M. Mendel and K.S. Fu, Adaptive, learning, and pattern recognitionsystems: theory and applications, Academic Press, New York, 1970.

 M. Minsky and S. Papert, Perceptrons: An Introduction toComputational Geometry, MIT Press, Cambridge, Mass, 1969.

 A.G. Arkadev and E.M. Braverman, Teaching Computers to RecognizePatterns, Academic Press, London, 1966.

 Nilsson, N.J., Learning Machines, McGraw-Hill, New York, 1965.

 G.S. Sebestyen, Decision-Making Processes in Pattern Recognition,Macmillan, New York, 1962.

 Rosenblatt, F., Principles of Neurodynamics: Perceptrons and thetheory of brain mechanisms, Spartan Books, Washington, D.C., 1962.

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计算机视觉文献索引目录

Annotated Computer Vision Bibliography: Table ofContents

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在线讲义:

  1. I.T. Young, J.J. Gerbrands, J. van Vliet, Delft University, The Netherlands,

Image Processing Fundamentals(图象处理基础)

WebAddress:http://www.ph.tn.tudelft.nl/Courses/FIP

  1. Milan Sonka, University of Iowa,

Digital Image Processing

WebAddress:http://www.engineering.uiowa.edu/~dip/LECTURE/contents.html

  1. Clifford Watson,Department of Applied Mathematics, University of Washington, Seattle, Washington 98195

An Image Processing Tutorial for Beginning Undergraduate Students(图象处理入门)

WebAddress:http://www.cs.washington.edu/research/metip/tutor/tutor.html


相关领域其它重要论文专题链接

重要网址


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清华大学计算机系 艾海舟

最近修改时间:2000年4月4日

 


出处:http://media.cs.tsinghua.edu.cn/~ahz/digitalimageprocess/CourseImageProcess.html

2018-01-29 10:43:26 zhu_hongji 阅读数 1359

转自http://blog.csdn.net/passball/article/details/42805269

1. 数学

我们所说的图像处理实际上就是数字图像处理,是把真实世界中的连续三维随机信号投影到传感器的二维平面上,采样并量化后得到二维矩阵。数字图像处理就是二维矩阵的处理,而从二维图像中恢复出三维场景就是计算机视觉的主要任务之一。这里面就涉及到了图像处理所涉及到的三个重要属性:连续性,二维矩阵,随机性。所对应的数学知识是高等数学(微积分),线性代数(矩阵论),概率论和随机过程。这三门课也是考研的三门课,构成了图像处理和计算机视觉最基础的数学基础。如果想要更进一步,就要到网上搜搜林达华推荐的数学数目了。


2. 信号处理

图像处理其实就是二维和三维信号处理,而处理的信号又有一定的随机性,因此经典信号处理和随机信号处理都是图像处理和计算机视觉中必备的理论基础。


2.1经典信号处理

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)

 

2.2随机信号处理

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

 

2.3 小波变换

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

 

2.4 信息论

信息论基础(原书第2版) Thomas M.Cover等著 阮吉寿等译


3. 模式识别

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


4. 图像处理与计算机视觉的书籍推荐

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

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

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 RichardSzeliski著

Computer Vision: Theory and Algorithm

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。


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。网上也可以找到电子版。


计算机视觉:一种现代方法 DAForsyth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。第二版已经在今年(2012年)出来了,在iask上可以找到非常清晰的版本,将近800页,补充了很多内容。期待影印版。


Machine vision: theory,algorithms, practicalities 第三版 Davies著

为数不多的英国人写的书,偏向于工业。


数字图像处理 第四版 Pratt著

Digital Image Processing

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。


5 小结

罗嗦了这么多,实际上就是几个建议:

(1)基础书千万不可以扔,也不能低价处理给同学或者师弟师妹。不然到时候还得一本本从书店再买回来的。钱是一方面的问题,对着全新的书看完全没有看自己当年上过的课本有感觉。

(2)遇到有相关的课,果断选修或者蹭之,比如随机过程,小波分析,模式识别,机器学习,数据挖掘,现代信号处理甚至泛函。多一些理论积累对将来科研和工作都有好处。

(3)资金允许的话可以多囤一些经典的书,有的时候从牙缝里面省一点都可以买一本好书。不过千万不要像我一样只囤不看。

图像处理与计算机视觉:基础,经典以及最近发展(3)计算机视觉中的信号处理与模式识别

Last Update: 2012-6-23


从本章开始,进入本文的核心章节。一共分三章,分别讲述信号处理与模式识别,图像处理与分析以及计算机视觉。与其说是讲述,不如说是一些经典文章的罗列以及自己的简单点评。与前一个版本不同的是,这次把所有的文章按类别归了类,并且增加了很多文献。分类的时候并没有按照传统的分类方法,而是划分成了一个个小的门类,比如SIFT,Harris都作为了单独的一类,虽然它们都可以划分到特征提取里面去。这样做的目的是希望能突出这些比较实用且比较流行的方法。为了以后维护的方法,按照字母顺序排的序。

本章的下载地址在:

http://iask.sina.com.cn/u/2252291285/ish?folderid=868770

1.  Boosting


Boosting是最近十来年来最成功的一种模式识别方法之一,个人认为可以和SVM并称为模式识别双子星。它真正实现了“三个臭皮匠,赛过诸葛亮”。只要保证每个基本分类器的正确率超过50%,就可以实现组合成任意精度的分类器。这样就可以使用最简单的线性分类器。Boosting在计算机视觉中的最成功的应用无疑就是Viola-Jones提出的基于Haar特征的人脸检测方案。听起来似乎不可思议,但Haar+Adaboost确实在人脸检测上取得了巨大的成功,已经成了工业界的事实标准,并且逐步推广到其他物体的检测。

Rainer Lienhart在2002 ICIP发表的这篇文章是Haar+Adaboost的最好的扩展,他把原始的两个方向的Haar特征扩展到了四个方向,他本人是OpenCV积极的参与着。现在OpenCV的库里面实现的Cascade Classification就包含了他的方法。这也说明了盛会(如ICIP,ICPR,ICASSP)也有好文章啊,只要用心去发掘。


[1997] A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting

[1998] Boosting the margin A new explanation for the effectiveness of voting methods

[2002 ICIP TR] Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid ObjectDetection

[2003] The Boosting Approach to Machine Learning An Overview

[2004 IJCV] Robust Real-time Face Detection


2. Clustering


聚类主要有K均值聚类,谱聚类和模糊聚类。在聚类的时候如果自动确定聚类中心的数目是一个一直没有解决的问题。不过这也很正常,评价标准不同,得到的聚类中心数目也不一样。不过这方面还是有一些可以参考的文献,在使用的时候可以基于这些方法设计自己的准则。关于聚类,一般的模式识别书籍都介绍的比较详细,不过关于cluster validity讲的比较少,可以参考下面的文章看看。


[1989 PAMI] Unsupervised Optimal Fuzzy Clustering

[1991 PAMI] A validity measure for fuzzy clustering

[1995 PAMI] On cluster validity for the fuzzy c-means model

[1998] Some New Indexes of Cluster Validity

[1999 ACM] Data Clustering A Review

[1999 JIIS] On Clustering Validation Techniques

[2001] Estimating the number of clusters in a dataset via the Gap statistic

[2001 NIPS] On Spectral Clustering

[2002] A stability based method for discovering structure in clustered data

[2007] A tutorial on spectral clustering


3.  Compressive Sensing


最近大红大紫的压缩感知理论。


[2006 TIT] Compressed Sensing

[2008 SPM] An Introduction to Compressive Sampling

[2011 TSP] Structured Compressed Sensing From Theory to Applications


4. Decision Trees


对决策树感兴趣的同学这篇文章是非看不可的了。


[1986] Introduction to Decision Trees


5. Dynamical Programming


动态规划也是一个比较使用的方法,这里挑选了一篇PAMI的文章以及一篇Book Chapter


[1990 PAMI] using dynamic programming for solving variational problems in vision

[Book Chapter] Dynamic Programming


6.  Expectation Maximization


EM是计算机视觉中非常常见的一种方法,尤其是对参数的估计和拟合,比如高斯混合模型。EM和GMM在Bishop的PRML里单独的作为一章,讲的很不错。关于EM的tutorial,网上也可以搜到很多。


[1977] Maximum likelihood from incomplete data via the EM algorithm

[1996 SPM] The Expectation-Maximzation Algorithm


7.  Graphical Models


伯克利的乔丹大仙的Graphical Model,可以配合这Bishop的PRML一起看。


[1999 ML] An Introduction to Variational Methods for Graphical Models


8. Hidden Markov Model


HMM在语音识别中发挥着巨大的作用。在信号处理和图像处理中也有一定的应用。最早接触它是跟小波和检索相关的,用HMM来描述小波系数之间的相互关系,并用来做检索。这里提供一篇1989年的经典综述,几篇HMM在小波,分割,检索和纹理上的应用以及一本比较早的中文电子书,现在也不知道作者是谁,在这里对作者表示感谢。


[1989 ] A tutorial on hidden markov models and selected applications in speech recognition

[1998 TSP] Wavelet-based statistical signal processing using hidden Markov models

[2001 TIP] Multiscale image segmentation using wavelet-domain hidden Markov models

[2002 TMM] Rotation invariant texture characterization and retrieval using steerable wavelet-domain hiddenMarkov models

[2003 TIP] Wavelet-based texture analysis and synthesis using hidden Markov models

Hmm Chinese book.pdf


9.  Independent Component Analysis


同PCA一样,独立成分分析在计算机视觉中也发挥着重要的作用。这里介绍两篇综述性的文章,最后一篇是第二篇的TR版本,内容差不多,但比较清楚一些。


[1999] Independent Component Analysis A Tutorial

[2000 NN] Independent component analysis algorithms and applications

[2000] Independent Component Analysis Algorithms and Applications


10. Information Theory


计算机视觉中的信息论。这方面有一本很不错的书Information Theory in Computer Vision and Pattern Recognition。这本书有电子版,如果需要用到的话,也可以参考这本书。


[1995 NC] An Information-Maximization Approach to Blind Separation and Blind Deconvolution

[2010] An information theory perspective on computational vision


11.  Kalman Filter


这个话题在张贤达老师的现代信号处理里面讲的比较深入,还给出了一个有趣的例子。这里列出了Kalman的最早的论文以及几篇综述,还有Unscented Kalman Filter。同时也有一篇Kalman Filter在跟踪中的应用以及两本电子书。


[1960 Kalman] A New Approach to Linear Filtering and Prediction Problems Kalman

[1970] Least-squares estimation_from Gauss to Kalman

[1997 SPIE] A New Extension of the Kalman Filter to Nonlinear System

[2000] The Unscented Kalman Filter for Nonlinear Estimation

[2001 Siggraph] An Introduction to the Kalman Filter_full

[2003] A Study of the Kalman Filter applied to Visual Tracking


12.  Pattern Recognition and Machine Learning


模式识别名气比较大的几篇综述


[2000 PAMI] Statistical pattern recognition a review

[2004 CSVT] An Introduction to Biometric Recognition

[2010 SPM] Machine Learning in Medical Imaging


13. Principal Component Analysis


著名的PCA,在特征的表示和特征降维上非常有用。


[2001 PAMI] PCA versus LDA

[2001] Nonlinear component analysisas a kernel eigenvalue problem

[2002] A Tutorial on Principal Component Analysis

[2004 PAMI] Two-dimensional PCA a new approach to appearance-based face representation and recognition

[2009] A Tutorial on Principal Component Analysis

[2011] Robust Principal Component Analysis

[Book Chapter] Singular Value Decomposition and Principal Component Analysis


14.  Random Forest


随机森林


[2001 ML] Random Forests


15.      RANSAC


随机抽样一致性方法,与传统的最小均方误差等完全是两个路子。在Sonka的书里面也有提到。


[2009 BMVC] Performance Evaluation of RANSAC Family


16.      Singular Value Decomposition

对于非方阵来说,就是SVD发挥作用的时刻了。一般的模式识别书都会介绍到SVD。这里列出了K-SVD以及一篇BookChapter

[2006 TSP] K-SVD An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

[Book Chapter] Singular Value Decomposition and Principal Component Analysis


17.  Sparse Representation


这里主要是Proceeding of IEEE上的几篇文章


[2009 PAMI] Robust Face Recognition via Sparse Representation

[2009 PIEEE] Image Decomposition and Separation Using Sparse Representations An Overview

[2010 PIEEE] Dictionaries for Sparse Representation Modeling

[2010 PIEEE] It's All About the Data

[2010 PIEEE] Matrix Completion With Noise

[2010 PIEEE] On the Role of Sparse and Redundant Representations in Image Processing

[2010 PIEEE] Sparse Representation for Computer Vision and Pattern Recognition

[2011 SPM] Directionary Learning


18.   Support Vector Machines

[1998] A Tutorial on Support Vector Machines for Pattern Recognition

[2004] LIBSVM A Library for Support Vector Machines


19.  Wavelet

在小波变换之前,时频分析的工具只有傅立叶变换。众所周知,傅立叶变换在时域没有分辨率,不能捕捉局部频域信息。虽然短时傅立叶变换克服了这个缺点,但只能刻画恒定窗口的频率特性,并且不能很好的扩展到二维。小波变换的出现很好的解决了时频分析的问题,作为一种多分辨率分析工具,在图像处理中得到了极大的发展和应用。在小波变换的发展过程中,有几个人是不得不提的,Mallat, Daubechies,Vetteri, M.N.Do, Swelden,Donoho。Mallat和Daubechies奠定了第一代小波的框架,他们的著作更是小波变换的必读之作,相对来说,小波十讲太偏数学了,比较难懂。而Mallat的信号处理的小波导引更偏应用一点。Swelden提出了第二代小波,使小波变换能够快速方便的实现,他的功劳有点类似于FFT。而Donoho,Vetteri,Mallat及其学生们提出了Ridgelet, Curvelet, Bandelet,Contourlet等几何小波变换,让小波变换有了方向性,更便于压缩,去噪等任务。尤其要提的是M.N.Do,他是一个越南人,得过IMO的银牌,在这个领域著作颇丰。我们国家每年都有5个左右的IMO金牌,希望也有一两个进入这个领域,能够也让我等也敬仰一下。而不是一股脑的都进入金融,管理这种跟数学没有多大关系的行业,呵呵。很希望能看到中国的陶哲轩,中国的M.N.Do。


说到小波,就不得不提JPEG2000。在JPEG2000中使用了Swelden和Daubechies提出的用提升算法实现的9/7小波和5/3小波。如果对比JPEG和JPEG2000,就会发现JPEG2000比JPEG在性能方面有太多的提升。本来我以为JPEG2000的普及只是时间的问题。但现在看来,这个想法太Naive了。现在已经过去十几年了,JPEG2000依然没有任何出头的迹象。不得不说,工业界的惯性力量太强大了。如果以前的东西没有什么硬伤的话,想改变太难了。不巧的是,JPEG2000的种种优点在最近的硬件上已经有了很大的提升。压缩率?现在动辄1T,2T的硬盘,没人太在意压缩率。渐进传输?现在的网速包括无线传输的速度已经相当快了,渐进传输也不是什么优势。感觉现在做图像压缩越来越没有前途了,从最近的会议和期刊文档也可以看出这个趋势。不管怎么说,JPEG2000的Overview还是可以看看的。


[1989 PAMI] A theory for multiresolution signal decomposition__the wavelet representation

[1996 PAMI] Image Representation using 2D Gabor Wavelet

[1998 ] FACTORING WAVELET TRANSFORMSIN TO LIFTING STEPS

[1998] The Lifting Scheme_ A Construction Of Second Generation Wavelets

[2000 TCE] The JPEG2000 still image coding system_ an overview

[2002 TIP] The curvelet transform for image denoising

[2003 TIP] Gray and color imagecontrast enhancement by the curvelet transform

[2003 TIP] Mathematical Properties of the jpeg2000 wavelet filters

[2003 TIP] The finite ridgelet transform for image representation

[2005 TIP] Sparse Geometric Image Representations With Bandelets

[2005 TIP] The Contourlet Transform_ An Efficient Directional Multiresolution Image Representation

[2010 SPM] The Curvelet Transform


图像处理与计算机视觉:基础,经典以及最近发展(4)图像处理与分析

Last update: 2012-6-3

本章主要讨论图像处理与分析。虽然后面计算机视觉部分的有些内容比如特征提取等也可以归结到图像分析中来,但鉴于它们与计算机视觉的紧密联系,以及它们的出处,没有把它们纳入到图像处理与分析中来。同样,这里面也有一些也可以划归到计算机视觉中去。这都不重要,只要知道有这么个方法,能为自己所用,或者从中得到灵感,这就够了。

本章的下载地址在:

http://iask.sina.com.cn/u/2252291285/ish?folderid=868771


1. Bilateral Filter

Bilateral Filter俗称双边滤波器是一种简单实用的具有保持边缘作用的平缓滤波器,由Tomasi等在1998年提出。它现在已经发挥着重大作用,尤其是在HDR领域。

[1998 ICCV] BilateralFiltering for Gray and Color Images

[2008 TIP] AdaptiveBilateral Filter for Sharpness Enhancement and Noise Removal


2. Color

如果对颜色的形成有一定的了解,能比较深刻的理解一些算法。这方面推荐冈萨雷斯的数字图像处理中的相关章节以及Sharma在Digital Color Imaging Handbook中的第一章“Colorfundamentals for digital imaging”。跟颜色相关的知识包括Gamma,颜色空间转换,颜色索引以及肤色模型等,这其中也包括著名的EMD。

[1991 IJCV] Color Indexing

[2000 IJCV] The EarthMover's Distance as a Metric for Image Retrieval

[2001 PAMI] Colorinvariance

[2002 IJCV] StatisticalColor Models with Application to Skin Detection

[2003] A review of RGBcolor spaces

[2007 PR]A survey ofskin-color modeling and detection methods

Gamma.pdf

GammaFAQ.pdf


3.Compression and Encoding

个人以为图像压缩编码并不是当前很热的一个话题,原因前面已经提到过。这里可以看看一篇对编码方面的展望文章

[2005 IEEE] Trends andperspectives in image and video coding


4.Contrast Enhancement

对比度增强一直是图像处理中的一个恒久话题,一般来说都是基于直方图的,比如直方图均衡化。冈萨雷斯的书里面对这个话题讲的比较透彻。这里推荐几篇个人认为不错的文章。

[2002 IJCV] Vision and theAtmosphere

[2003 TIP] Gray and colorimage contrast enhancement by the curvelet transform

[2006 TIP] Gray-levelgrouping (GLG) an automatic method for optimized image contrastenhancement-part II

[2006 TIP] Gray-levelgrouping (GLG) an automatic method for optimized image contrastEnhancement-part I

[2007 TIP] TransformCoefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

[2009 TIP] A HistogramModification Framework and Its Application for Image Contrast Enhancement


5. Deblur (Restoration)

图像恢复或者图像去模糊一直是一个非常难的问题,尤其是盲图像恢复。港中文的jiaya jia老师在这方面做的不错,他在主页也给出了exe。这方面的内容也建议看冈萨雷斯的书。这里列出了几篇口碑比较好的文献,包括古老的Richardson-Lucy方法,几篇盲图像恢复的综述以及最近的几篇文章,尤以Fergus和Jiaya Jia的为经典。

[1972] Bayesian-BasedIterative Method of Image Restoration

[1974] an iterative techniquefor the rectification of observed distributions

[1990 IEEE] Iterativemethods for image deblurring

[1996 SPM] Blind ImageDeconvolution

[1997 SPM] Digital imagerestoration

[2005] Digital ImageReconstruction - Deblurring and Denoising

[2006 Siggraph] RemovingCamera Shake from a Single Photograph

[2008 Siggraph]High-quality Motion Deblurring from a Single Image

[2011 PAMI]Richardson-Lucy Deblurring for Scenes under a Projective Motion Path


6. Dehazing and Defog

严格来说去雾化也算是图像对比度增强的一种。这方面最近比较好的工作就是He kaiming等提出的Dark Channel方法。这篇论文也获得了2009的CVPR 最佳论文奖。2003年的广东高考状元已经于2011年从港中文博士毕业加入MSRA(估计当时也就二十五六岁吧),相当了不起。

[2008 Siggraph] SingleImage Dehazing

[2009 CVPR] Single ImageHaze Removal Using Dark Channel Prior

[2011 PAMI] Single ImageHaze Removal Using Dark Channel Prior


7. Denoising

图像去噪也是图像处理中的一个经典问题,在数码摄影中尤其重要。主要的方法有基于小波的方法和基于偏微分方程的方法。

[1992 SIAM] Imageselective smoothing and edge detection by nonlinear diffusion. II

[1992 SIAM] Imageselective smoothing and edge detection by nonlinear diffusion

[1992] Nonlinear totalvariation based noise removal algorithms

[1994 SIAM] Signal andimage restoration using shock filters and anisotropic diffusion

[1995 TIT] De-noising bysoft-thresholding

[1998 TIP] Orientationdiffusions

[2000 TIP] Adaptivewavelet thresholding for image denoising and compression

[2000 TIP] Fourth-orderpartial differential equations for noise removal

[2001] Denoising  through wavelet shrinkage

[2002 TIP] The CurveletTransform for Image Denoising

[2003 TIP] Noise removalusing fourth-order partial differential equation with applications to medicalmagnetic resonance images in space and time

[2008 PAMI] AutomaticEstimation and Removal of Noise from a Single Image

[2009 TIP] Is DenoisingDead


8. Edge Detection

边缘检测也是图像处理中的一个基本任务。传统的边缘检测方法有基于梯度算子,尤其是Sobel算子,以及经典的Canny边缘检测。到现在,Canny边缘检测及其思想仍在广泛使用。关于Canny算法的具体细节可以在Sonka的书以及canny自己的论文中找到,网上也可以搜到。最快最直接的方法就是看OpenCV的源代码,非常好懂。在边缘检测方面,Berkeley的大牛J Malik和他的学生在2004年的PAMI提出的方法效果非常好,当然也比较复杂。在复杂度要求不高的情况下,还是值得一试的。MIT的Bill Freeman早期的代表作Steerable Filter在边缘检测方面效果也非常好,并且便于实现。这里给出了几篇比较好的文献,包括一篇最新的综述。边缘检测是图像处理和计算机视觉中任何方向都无法逃避的一个问题,这方面研究多深都不为过。

[1980] theory of edgedetection

[1983 Canny Thesis] findedge

[1986 PAMI] AComputational Approach to Edge Detection

[1990 PAMI] Scale-spaceand edge detection using anisotropic diffusion

[1991 PAMI] The design anduse of steerable filters

[1995 PR] Multiresolutionedge detection techniques

[1996 TIP] Optimal edgedetection in two-dimensional images

[1998 PAMI] Local ScaleControl for Edge Detection and Blur Estimation

[2003 PAMI] Statisticaledge detection_ learning and evaluating edge cues

[2004 IEEE] Edge DetectionRevisited

[2004 PAMI] Design ofsteerable filters for feature detection using canny-like criteria

[2004 PAMI] Learning toDetect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues

[2011 IVC] Edge and lineoriented contour detection State of the art


9. Graph Cut

基于图割的图像分割算法。在这方面没有研究,仅仅列出几篇引用比较高的文献。这里又见J Malik,当然还有华人杰出学者Jianbo Shi,他的主页非常搞笑,在醒目的位置标注Do not flyChina Eastern Airlines ... 看来是被坑过,而且坑的比较厉害。这个领域,俄罗斯人比较厉害。

[2000 PAMI] Normalizedcuts and image segmentation

[2001 PAMI] Fastapproximate energy minimization via graph cuts

[2004 PAMI] What energyfunctions can be minimized via graph cuts


10.Hough Transform

虽然霍夫变换可以扩展到广义霍夫变换,但最常用的还是检测圆和直线。这方面同样推荐看OpenCV的源代码,一目了然。Matas在2000年提出的PPHT已经集成到OpenCV中去了。

[1986 CVGIU] A Survey ofthe Hough Transform

[1989] A Comparative studyof Hough transform methods for circle finding

[1992 PAMI] Shapesrecognition using the straight line Hough transform_ theory and generalization

[1997 PR] Extraction ofline features in a noisy image

[2000 CVIU] RobustDetection of Lines Using the Progressive Probabilistic Hough Transform


11. Image Interpolation

图像插值,偶尔也用得上。一般来说,双三次也就够了

[2000 TMI] Interpolationrevisited


12. Image Matting

也就是最近,我才知道这个词翻译成中文是抠图,比较难听,不知道是谁开始这么翻译的。没有研究,请看文章以及Richard Szeliski的相关章节。以色列美女Levin在这方面有两篇PAMI。

[2008 Fnd] Image and VideoMatting A Survey

[2008 PAMI] A Closed-FormSolution to Natural Image Matting

[2008 PAMI] SpectralMatting


13.  Image Modeling

图像的统计模型。这方面有一本专门的著作Natural Image Statistics

[1994] The statistics ofnatural images

[2003 JMIV] On Advances inStatistical Modeling of Natural Images

[2009 IJCV] Fields ofExperts

[2009 PAMI] Modelingmultiscale subbands of photographic images with fields of Gaussian scalemixtures


14. Image Quality Assessment

在图像质量评价方面,Bovik是首屈一指的。这位老师也很有意思,作为编辑出版了很多书。他也是IEEE的Fellow

[2004 TIP] Image qualityassessment from error visibility to structural similarity

[2011 TIP] blind imagequality assessment From Natural Scene Statistics to Perceptual Quality


15.  Image Registration

图像配准最早的应用在医学图像上,在图像融合之前需要对图像进行配准。在现在的计算机视觉中,配准也是一个需要理解的概念,比如跟踪,拼接等。在KLT中,也会涉及到配准。这里主要是综述文献。

[1992 MIA] Image matching asa diffusion process

[1992 PAMI] A Method forRegistration of 3-D shapes

[1992] a survey of imageregistration techniques

[1998 MIA] A survey ofmedical image registration

[2003 IVC] Imageregistration methods a survey

[2003 TMI]Mutual-Information-Based Registration of Medical Survey

[2011 TIP] Hairisregistration


16. Image Retrieval

图像检索曾经很热,在2000年之后似乎消停了一段时间。最近各种图像的不变性特征提出来之后,再加上互联网搜索的商业需求,这个方向似乎又要火起来了,尤其是在工业界。这仍然是一个非常值得关注的方面。而且图像检索与目标识别具有相通之处,比如特征提取和特征降维。这方面的文章值得一读。在最后给出了两篇Book chapter,其中一篇还是中文的。

[2000 PAMI] Content-basedimage retrieval at the end of the early years

[2000 TIP] PicToSeekCombining Color and Shape Invariant Features for Image Retrieval

[2002] Content-Based ImageRetrieval Systems A Survey

[2008] Content-Based ImageRetrieval-Literature Survey

[2010] Plant ImageRetrieval Using Color,Shape and Texture Features

[2012 PAMI] A MultimediaRetrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback

CBIR Chinese

fundament of cbir


17. Image Segmentation

图像分割,非常基本但又非常难的一个问题。建议看Sonka和冈萨雷斯的书。这里给出几篇比较好的文章,再次看到了J Malik。他们给出了源代码和测试集,有兴趣的话可以试试。

[2004 IJCV] EfficientGraph-Based Image Segmentation

[2008 CVIU] Imagesegmentation evaluation A survey of unsupervised methods

[2011 PAMI] ContourDetection and Hierarchical Image Segmentation


18. Level Set

大名鼎鼎的水平集,解决了Snake固有的缺点。Level set的两位提出者Sethian和Osher最后反目,实在让人遗憾。个人以为,这种方法除了迭代比较费时,在真实场景中的表现让人生疑。不过,2008年ECCV上的PWP方法在结果上很吸引人。在重初始化方面,Chunming Li给出了比较好的解决方案

[1995 PAMI] Shape modelingwith front propagation_ a level set approach

[2001 JCP] Level SetMethods_ An Overview and Some Recent Results

[2005 CVIU] Geodesicactive regions and level set methods for motion estimation and tracking

[2007 IJCV] A Review ofStatistical Approaches to Level Set Segmentation

[2008 ECCV] RobustReal-Time Visual Tracking using Pixel-Wise Posteriors

[2010 TIP] DistanceRegularized Level Set Evolution and its Application to Image Segmentation


19.Pyramid

其实小波变换就是一种金字塔分解算法,而且具有无失真重构和非冗余的优点。Adelson在1983年提出的Pyramid优点是比较简单,实现起来比较方便。

[1983] The LaplacianPyramid as a Compact Image Code


20. Radon Transform

Radon变换也是一种很重要的变换,它构成了图像重建的基础。关于图像重建和radon变换,可以参考章毓晋老师的书,讲的比较清楚。

[1993 PAMI] Imagerepresentation via a finite Radon transform

[1993 TIP] The fastdiscrete radon transform I theory

[2007 IVC] Generalisedfinite radon transform for N×N images


21.Scale Space

尺度空间滤波在现代不变特征中是一个非常重要的概念,有人说SIFT的提出者Lowe是不变特征之父,而Linderburg是不变特征之母。虽然尺度空间滤波是Witkin最早提出的,但其理论体系的完善和应用还是Linderburg的功劳。其在1998年IJCV上的两篇文章值得一读,不管是特征提取方面还是边缘检测方面。

[1987] Scale-spacefiltering

[1990 PAMI] Scale-Spacefor Discrete Signals

[1994] Scale-space theoryA basic tool for analysing structures at different scales

[1998 IJCV] Edge Detectionand Ridge Detection with Automatic Scale Selection

[1998 IJCV] FeatureDetection with Automatic Scale Selection


22. Snake

活动轮廓模型,改变了传统的图像分割的方法,用能量收缩的方法得到一个统计意义上的能量最小(最大)的边缘。

[1987 IJCV] Snakes ActiveContour Models

[1996 ] deformable modelin medical image A Survey

[1997 IJCV] geodesicactive contour

[1998 TIP] Snakes, shapes,and gradient vector flow

[2000 PAMI] Geodesic activecontours and level sets for the detection and tracking of moving objects

[2001 TIP] Active contourswithout edges


23.  Super Resolution

超分辨率分析。对这个方向没有研究,简单列几篇文章。其中Yang Jianchao的那篇在IEEE上的下载率一直居高不下。

[2002] Example-BasedSuper-Resolution

[2003 SPM] Super-Resolution Image Reconstruction A Technical Overview

[2009 ICCV] Super-Resolutionfrom a Single Image

[2010 TIP] ImageSuper-Resolution Via Sparse Representation


24. Thresholding

阈值分割是一种简单有效的图像分割算法。这个topic在冈萨雷斯的书里面讲的比较多。这里列出OTSU的原始文章以及一篇不错的综述。

[1979 IEEE] OTSU Athreshold selection method from gray-level histograms

[2001 JISE] A Fast Algorithmfor Multilevel Thresholding

[2004 JEI] Survey overimage thresholding techniques and quantitative performance evaluation


25. Watershed

分水岭算法是一种非常有效的图像分割算法,它克服了传统的阈值分割方法的缺点,尤其是Marker-Controlled Watershed,值得关注。Watershed在冈萨雷斯的书里面讲的比较详细。

[1991 PAMI] Watersheds indigital spaces an efficient algorithm based on immersion simulations

[2001]The WatershedTransform Definitions, Algorithms and Parallelizat on Strategies

2014-05-04 19:03:15 guangmingsky 阅读数 4068

最近版上有不少人在讨论图像处理的就业方向,似乎大部分都持悲观的态度。我想结合我今年找工作的经验谈谈我的看法。就我看来,个人觉得图像处理的就业还是不错的。首先可以把图像看成二维、三维或者更高维的信号,从这个意义上来说,图像处理是整个信号处理里面就业形势最好的,因为你不仅要掌握一维信号处理的基本知识,也要掌握图像处理的知识。其次,图像处理是计算机视觉和视频处理的基础,掌握好了图像处理的基本知识,就业时就可以向这些方向发展。目前的模式识别,大部分也都是图像模式识别。在实际应用场合,采集的信息很多都是图像信息,比如指纹、条码、人脸、虹膜、车辆等等。说到应用场合,千万不能忘了医学图像这一块,如果有医学图像处理的背景,去一些医疗器械公司或者医疗软件公司也是不错的选择。图像处理对编程的要求比较高,如果编程很厉害,当然就业也多了一个选择方向,并不一定要局限在图像方向。
下面谈谈我所知道的一些公司信息,不全,仅仅是我所了解到的或者我所感兴趣的,实际远远不止这么多。
搜索方向
基于内容的图像或视频搜索是很多搜索公司研究的热点。要想进入这个领域,必须有很强的编程能力,很好的图像处理和模式识别的背景。要求高待遇自然就不错,目前这方面的代表公司有微软、googleyahoo和百度,个个鼎鼎大名。
医学图像方向
目前在医疗器械方向主要是几大企业在竞争,来头都不小,其中包括西门子、飞利浦和柯达,主要生产CTMRI等医疗器材。由于医疗器械的主要功能是成像,必然涉及到对图像的处理,做图像处理的很有机会进入这些公司。它们在国内都设有研发中心,simens的在上海和深圳,GE和柯达都在上海,飞利浦的在沈阳。由于医疗市场是一个没有完全开发的市场,而一套医疗设备的价格是非常昂贵的,所以在这些地方的待遇都还可以,前景也看好。国内也有一些这样的企业比如深圳安科和迈瑞。
模式识别方向
我没去调研过有哪些公司在做,但肯定不少,比如指纹识别、人脸识别、虹膜识别。还有一个很大的方向是车牌识别,这个我倒是知道有一个公司高德威智能交通似乎做的很不错的样子。目前视频监控是一个热点问题,做跟踪和识别的可以在这个方向找到一席之地。上海法视特位于上海张江高科技园区,在视觉和识别方面做的不错。北京的我也知道两个公司:大恒和凌云,都是以图像作为研发的主体。
视频方向
一般的高校或者研究所侧重在标准的制定和修改以及技术创新方面,而公司则侧重在编码解码的硬件实现方面。一般这些公司要求是熟悉或者精通MPEGH.264或者AVS,选择了这个方向,只要做的还不错,基本就不愁饭碗。由于这不是我所感兴趣的方向,所以这方面的公司的信息我没有收集,但平常在各个论坛或者各种招聘网站经常看到。我所知道的两个公司:诺基亚和pixelworks。


其实一般来说,只要涉及到成像或者图像的基本都要图像处理方面的人。比方说一个成像设备,在输出图像之前需要对原始图像进行增强或者去噪处理,存储时需要对图像进行压缩,成像之后需要对图像内容进行自动分析,这些内容都是图像处理的范畴。下面列举一些与图像有关或者招聘时明确说明需要图像处理方面人才的公司:上海豪威集成电路有限公司、中芯微、摩托罗拉上海研究院、威盛、松下、索尼、清华同方、三星。所有与图像(静止或者运动图像)有关的公司都是一种选择。比如数码相机、显微镜成像、超声成像、工业机器人控制、显示器、电视、遥感等等,都可以作为求职方向。
要求:
1
、外语。如果进外企,外语的重要性不言而喻。一般外企的第一轮面试都是英语口语面试。
2
、编程。这方面尤以C++为重,很多公司的笔试都是考c++知识。
3
、专业水平。如果要找专业相关的工作,研究生期间的研究经历和发表的论文就显的比较重要。
4
、知识面的宽度。我觉得在研究生期间,除了做好自己的研究方向之外,扩宽一下知识面也有很大的帮助,当然这个知识面指的是图像处理、计算机视觉和模式识别,知识面越宽,就业时的选择就会越多。
图像处理方向毕业的就业面非常广,而且待遇在应届生应该是中上等。其实还是一句话,能力决定一切。只要研究生三年没有白过,根本不愁找不到好工作。祝所有正在读研或者即将读研的朋友将来都能有一份满意的工作。
我说点不好的,版主的说法我同意都是正面的,反面的来说:现在大学和研究机构做图象的越来越多了,这里面老板自己懂图象的不知道有多少?!老板不懂,影响还是很大的。多数做图象的是用MATLAB,用别人的代码。在研究生三年学好C++毕业的有多少?在公司C++是重要的。图象其实就是信号处理,除了本科是学信号的以外,信号与系统、数字信号处理是一定要学好的,那相应的数学方面的概率,多元统计,甚至泛函也要了解。外语的基本要求是看懂英文文献(不一定全看懂),相应的英文书。去外企做研发,这是必备的。然后是口语和听力。说这些不是波冷水,希望大家了解清楚。
Compared to the number of jobs available each year in the imaging soceity, the people who are majoring on it are way too much. I have to say most of the people who studied the this area were not end up with working on this area anymore.
The most important thing here is to understand image processing, it requires a broad level of knowledge including, some math (algrebra, statistics, PDE), dsp, pattern recognition, programming skills...
It is all these background skills will find you a job, so prepare to have a deep understanding on all these areas related to image processing
我也是学模式识别的,但是研究方向是遥感图像处理和识别.总的来说这个方向是比较专,但也是目前图像处理中比较难做的一个方向,因为遥感图像的复杂性超过我们所见过的任何图像.
其实谈到就业问题,我觉得如果研究方向比较适合,特别是读研期间能到斑竹谈的那些牛比的公司实习,了解企业真正需要的方向可能做起来有目标性.
顺便提下:高德威公司还是不要考虑,因为本人在毕业面试过程中,虽然面试的人力资源人员很友善,但是通过他们老板写的一些文章可以发现他们还是一个比较自恋和自大的公司.
楼主是好人,不过此文更多是安慰,新手不可太当真
衡量专业好坏的标准有两个:应用前景和技术门槛。个人觉得图像处理应用前景一般,比通信,计算机差远了,而技术门槛,相信不是新手都清楚,比微波之类低不少。总的来说图像方向就业一般,it业算较冷得,特别是模式识别,人工智能之类,看起来高深邪乎,其实就是博士都不好找工作(亲身所见)

1)说到图像处理比通信差,很大部分的原因是当前行业背景,但通信真正的研发在中国又有多少,我的朋友中很多做工程的,况且现在在通信领域,很大的一个难点,也是多媒体通信。
2
)说到比计算机差,我觉得这与你怎么看待计算机专业有关,有人觉得是基础,是工具,有人觉得是专业。况且计算机那边,现在研究图像的也不少。
3
)再者,说微波,RFID等入门难,但要做精又谈何容易,而且兴趣真的很重要,没有兴趣,再有前景的专业,你也不一定能做好,还有女生并不适合搞这个,就业时,单位一般会暗示。另外,就业面也较窄,好公司真的难进,找工的时候,真的很郁闷,特别对女生。或许将来很大发展前途,这个另当别论。
4
)说回图像处理,我觉得还是较中肯的,略有好的嫌疑,关键还是在读研的时候能把方向做宽。一般做图像处理,需要何模式识别等相结合,拓宽知识面是必要的,在真正做研究的时候,也发现是必须的。研究点做深入,注重实现能力、创新能力和学习能力,通过论文多培养自己的材料组织提炼能力,锻炼逻辑思维。如果真的能做到三年光阴不虚度,找工应该不是问题,到时真正要考虑的是定位问题。
5
)当然,最后,找工的时候,包装是一种技巧,整合是一种需要。
我觉得做图像处理还是很有前途的。

 

作图像处理方面的研究工作,最重要的两个问题:其一是要把握住国际上最前沿的内容;其二是所作工作要具备很高的实用背景。解决第一个问题的办法就是找出这个方向公认最牛的几个超级大拿(看看他们都在作什么)和最权威的出版物(阅读上面最新的文献),解决第二个问题的办法是你最好能够找到一个实际应用的项目,边做边写文章。

做好这几点的途径之一就是充分利用网络资源,特别是权威网站和大拿们的个人主页。下面是我收集的一些资源,希望对大家有用。(这里我要感谢SMTH AI版的alamarik和Graphics版的faintt)

导航栏: [1]研究群体、[2]大拿主页、[3]前沿期刊、[4]GPL软件资源、[5]搜索引擎。

一、研究群体
http://www-2.cs.cmu.edu/~cil/vision.html
这是卡奈基梅隆大学的计算机视觉研究组的主页,上面提供很全的资料,从发表文章的下载到演示程序、测试图像、常用链接、相关软硬件,甚至还有一个搜索引擎。

http://www.cmis.csiro.au/IAP/zimage.htm
这是一个侧重图像分析的站点,一般。但是提供一个Image Analysis环境---ZIMAGE and SZIMAGE。

http://www.via.cornell.edu/
康奈尔大学的计算机视觉和图像分析研究组,好像是电子和计算机工程系的。侧重医学方面的研究,但是在上面有相当不错资源,关键是它正在建设中,能够跟踪一些信息。

http://www2.parc.com/istl/groups/did/didoverview.shtml
有一个很有意思的项目:DID(文档图像解码)。

http://www-cs-students.stanford.edu/
斯坦福大学计算机系主页,自己找吧:(

http://www.fmrib.ox.ac.uk/analysis/
主要研究:Brain Extraction Tool,Nonlinear noise reduction,Linear Image Registration,

Automated Segmentation,Structural brain change analysis,motion correction,etc.

http://www.cse.msu.edu/prip/
这是密歇根州立大学计算机和电子工程系的模式识别--图像处理研究组,它的FTP上有许多的文章(NEW)。

http://pandora.inf.uni-jena.de/p/e/index.html
德国的一个数字图像处理研究小组,在其上面能找到一些不错的链接资源。

http://www-staff.it.uts.edu.au/~sean/CVCC.dir/home.html
CVIP(used to be CVCC for Computer Vision and Cluster Computing) is a research group focusing on cluster-based computer vision within the Spiral Architecture.

http://cfia.gmu.edu/
The mission of the Center for Image Analysis is to foster multi-disciplinary research in image, multimedia and related technologies by establishing links

between academic institutes, industry and government agencies, and to transfer key technologies to

help industry build next

generation commercial and military imaging and multimedia systems.

http://peipa.essex.ac.uk/info/groups.html
可以通过它来搜索全世界各地的知名的计算机视觉研究组(CV Groups),极力推荐。

二、图像处理GPL库
http://www.ph.tn.tudelft.nl/~klamer/cppima.html
Cppima 是一个图像处理的C++函数库。这里有一个较全面介绍它的库函数的文档,当然你也可以下载压缩的GZIP包,里面包含TexInfo格式的文档。

http://iraf.noao.edu/
Welcome to the IRAF Homepage! IRAF is the Image Reduction and Analysis Facility, a general purpose software

system for the reduction and analysis of astronomical data.

http://entropy.brni-jhu.org/tnimage.html
一个非常不错的Unix系统的图像处理工具,看看它的截图。你可以在此基础上构建自己的专用图像处理工具包。

http://sourceforge.net/projects/
这是GPL软件集散地,到这里找你想要得到的IP库吧。

三、搜索资源
当然这里基本的搜索引擎还是必须要依靠的,比如Google等,可以到我常用的链接看看。下面的链接可能会节省你一些时间:

http://sal.kachinatech.com/
http://cheminfo.pku.edu.cn/mirrors/SAL/index.shtml
四、大拿网页
http://www.ai.mit.edu/people/wtf/
这位可是MIT人工智能实验室的BILL FREEMAN。大名鼎鼎!专长是:理解--贝叶斯模型。

http://www.merl.com/people/brand/
MERL(Mitsubishi Electric Research Laboratory)中的擅长“Style Machine”高手。

http://research.microsoft.com/~ablake/
CV界极有声望的A.Blake 1977年毕业于剑桥大学三一学院并或数学与电子科学学士学位。之后在MIT,Edinburgh,Oxford先后组建过研究小组并成为Oxford的教授,直到1999年进入微软剑桥研究中心。主要工作领域是计算机视觉。

http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/har/Web/home.html
这位牛人好像正在学习汉语,并且搜集了诸如“两只老虎(Two Tigers)”的歌曲,嘿嘿:)
他的主页上面还有几个牛:Shumeet Baluja, Takeo Kanade。他们的Face Detection作的绝对是世界一流。他毕业于卡奈基梅隆大学的计算机科学系,兴趣是计算机视觉。

http://www.ifp.uiuc.edu/yrui_ifp_home/html/huang_frame.html
这位老牛在1963年就获得了MIT的博士学位!他领导的Image Lab比较出名的是指纹识别。

五、前沿期刊(TOP10)
这里的期刊大部分都可以通过上面的大拿们的主页间接找到,在这列出主要是为了节省直接想找期刊投稿的兄弟的时间:)

IEEE Trans. On PAMIhttp://www.computer.org/tpami/index.htm
IEEE Transactionson Image Processing http://www.ieee.org/organizations/pubs/transactions/tip.htm
Pattern Recognition http://www.elsevier.com/locate/issn/00313203
Pattern Recognition Letters http://www.elsevier.com/locate/issn/01678655

2018-06-05 21:43:25 weixin_41036461 阅读数 5246

https://blog.csdn.net/zy122121cs/article/details/44860433


Colorization and Color Transfer(图像上色和颜色迁移)

Semantic Colorization with Internet Images, Chia et al. SIGGRAPH ASIA 2011 Color Harmonization, Cohen-Or, Sorkine, Gal, Leyvand, and Xu. Web Page Computing the alpha-Channel with Probabilistic Segmentation for Image Colorization, Dalmau-Cedeno, Rivera, and Mayorga Bayesian Color Constancy Revisited, Gehler, Rother, Blake, Minka, and Sharp Color2Gray: Salience-Preserving Color Removal, Gooch, Olsen, Tumblin, and Gooch Color Conceptualization, Hou and Zhang Light Mixture Estimation for Spatially Varying White Balance, Hsu, Mertens, Paris, Avidan, and Durand. Web Page Bayesian Correction of Image Intensity with Spatial Consideration, Jia, Sun, Tang, and Shum Robust Color-to-gray via Nonlinear Global Mapping, Kim, Jang, Demouth, and Lee. SIGGRAPH Asia 2009 Web Page Variational Models for Image Colorization via Chromaticity and Brightness Decomposition, Kang and March Colorization using Optimization, Levin, Lischinski, and Weiss
Intrinsic Colorization, Liu et al. SIGGRAPH ASIA 2008 Web Page
 N-Dimensional Probability Density Function Transfer and Its Application to Colour Transfer, Pitie et al. Automated Colour Grading using Colour Distribution Transfer, Pitie et al. Color by Linear Neighborhood Embedding, Qiu and Guan Manga Colorization, Qu, Wong, and Heng Color Transfer between Images, Reinhard, Ashikhmin, Gooch, and Shirley Local Color Transfer via Probabilistic Segmentation by Expectation-Maximization, Tai, Jia, and Tang
Data-Driven Image Color Theme Enhancement, Wang, Yu, Wong, Chen, and Xu. SIGGRAPH Asia 2010 Web Page
Color Transfer in Correlated Color Space, Xiao and Ma Fast Image and Video Colorization using Chrominance Blending, Yatziv and Sapiro

Texture Synthesis and Inpainting(纹理和成和修复)

Seam Carving for Content-Aware Image Resizing, Avidan and Shamir. Wikipedia Synthesizing Natural Textures, Ashikhmin PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing, Barnes, Shechtman, Finkelstein, and Goldman. SIGGRAPH 2009. Web Page Image Inpainting, Bertalmio, Sapiro, Caselles, and Ballester Video Watercolorization using Bidirectional Texture Advection, Bousseau, Neyret, Thollot, and Salesin
Camouflage Images, Chu et al. SIGGRAPH 2010 Web Page
 Object Removal by Exemplar-Based Inpainting, Criminisi, Perez, and Toyama Weiming DONG's web page contains useful information about texture synthesis and image resizing Image Quilting for Texture Synthesis and Transfer, Efros and Freeman Texture Synthesis by Non-parametric Sampling, Efros and Leung RotoTexture: Automated Tools for Texturing Raw Video, Fang and Hart Textureshop: Texture Synthesis as a Photograph Editing Tool, Fang and Hart Multiscale Texture Synthesis, Han, Risser, Ramamoorthi, and Grinspun Scene Completion Using Millions of Photographs, Hays and Efros Image Analogies, Hertzmann, Jacobs, Oliver, Curless, and Salesin Graphcut Textures: Image and Video Synthesis Using Graph Cuts, Kwatra , Schodl , Essa , Turk, and Bobick Improved Seam Carving for Video Retargeting, Rubinstein, Shamir, and Avidan. Video Multi-operator Media Retargeting, Rubinstein, Shamir, and Avidan. SIGGRAPH 2009. Web Page Fields of Experts: A Framework for Learning Image Priors, Roth and Black Curvature Regularity for Region-based Image Segmentation and Inpainting: A Linear Programming Relaxation, Schoenemann, Kahl, and Cremers. ICCV 2009. Fast Texture Synthesis using Tree-structured Vector Quantization, Wei and Levoy Non-homogeneous Content-driven Video-retargeting, Wolf, Guttmann, and Cohen-Or Feature Matching and Deformation for Texture Synthesis, Wu and Yu

HDR and Tone Mapping(高动态范围成像和色调映射)

Do HDR Displays Support LDR Content? A Psychophysical Evaluation, Akyu"z, Reinhard, Fleming, Riecke, Bu"lthoff Two-scale Tone Management for Photographic Look, Bae, Paris, and Durand Real-time Edge-Aware Image Processing with the Bilateral Grid, Chen, Paris, Durand Recovering High Dynamic Range Radiance Maps from Photographs, Debevec and Malik Fast Bilateral Filtering for the Display of High-Dynamic-Range Images, Durand and Dorsey Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation, Farbman, Fattal, Lischinski, and Szeliski. SIGGRAPH 2009. Web Page
Optimal HDR reconstruction with linear digital cameras, Granados et al., CVPR 2010.
 Gradient Domain High Dynamic Range Compression, Fattal, Lischinski, and Werman Modeling Human Color Perception under Extended Luminance Levels, Kim, Weyrich, and Kautz. SIGGRAPH 2009. Web Page Perceptually Based Tone Mapping for Low-Light Conditions, Kirk and O'Brien. SIGGRAPH 2011. Web Page Compressing and Companding High Dynamic Range Images with Subband Architectures, Li, Sharan, and Adelson Radiometric Calibration Using a Single Image Lin, Gu, Yamazaki, and Shum Determining the Radiometric Response Function from a Single Grayscale Image, Lin and Zhang Interactive Local Adjustment of Tonal Values, Lischinski, Farbman, Uyttendaele, and Szeliski. Web Page Exposure Fusion, Mertens, Kautz, Van Reeth Radiometric Self Calibration, Mitsunaga and Nayar Photographic Tone Reproduction for Digital Images, Reinhard, Stark, Shirley and Ferwerda Ldr2Hdr: On-the-Fly Reverse Tone Mapping of Legacy Video and Photographs, Rempel, Trentacoste, Seetzen, Young, Heidrich, Whitehead, and Ward High Dynamic Range Image Hallucination, Wang, Wei, Zhou, Guo, and Shum Fast, Robust Image Registration for Compositing High Dynamic Range Photographs from Hand-Held Exposures, Ward

Intrinsic Images(本征图像)

Removing Photography Artifacts using Gradient Projection and Flash-Exposure Sampling, Agrawal, Raskar, Nayar, and Li User-Assisted Intrinsic Images, Bousseau, Paris, and Durand. SIGGRAPH Asia 2009. Web Page Flash Photography Enhancement via Intrinsic Relighting, Eisemann and Durand Bayesian Model of Surface Perception, Freeman and Viola Detecting Illumination in Images, Finlayson, Fredembach, and Drew Ground Truth Dataset and Baseline Evaluations for Intrinsic Image AlgorithmsGrosse, Johnson, Adelson, and Freeman. ICCV 2009. A Variational Framework for Retinex, Kimmel, Elad, Shaked, Keshet, and Sobel Dark Flash Photography, Krishnan amd Fergus. SIGGRAPH 2009. Web Page Lightness and Retinex Theory, Land and McCann Estimating Intrinsic Images from Image Sequenceswith Biased Illumination, Matsushita, Lin, Kang, Shum. ECCV 2004 Post-production Facial Performance Relighting using Reflectance Transfer, Peers, Tamura, Matusik, and Debevec Separation of Highlight Reflections from Textured Surfaces, Tan, Lin, and Quan Recovering Intrinsic Images from a Single Image, Tappen, Freeman, and Adelson Estimating Intrinsic Component Images using Non-Linear Regression, Tappen, Adelson, and Freeman Deriving Intrinsic Images from Image Sequences, Weiss

Deblurring, Denoising, and Super-Resolution(图像去模糊,去噪和超分辨率)

Reinterpretable Imager: Towards Variable Post Capture Space, Angle & Time Resolution in Photography, Agrawal, Veeraraghavan, and Raskar. Eurographics 2010. Invertible Motion Blur in Video, Agrawal, Xu, and Raskar. SIGGRAPH 2009. Optimal Single Image Capture for Motion Deblurring, Agrawal and Raskar. CVPR 2009. Coded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility, Agrawal and Xu. CVPR 2009. A Non-local Algorithm for Image Denoising, Buades, Coll, and Morel. Analyzing Spatially-varying Blur, Chakrabarti, Zickler, and Freeman. CVPR 2010. Fast Motion Deblurring, Cho and Lee. SIGGRAPH Asia 2009. Web Page Motion Blur Removal with Orthogonal Parabolic Exposures, Cho, Levin, Durand, and Freeman. CVPR 2010. Web Page Handling Outliers in Non-Blind Image Deconvolution, Cho, Wang, and Lee. ICCV 2011. Web Page Display supersampling, Damera-Venkata and Chang Image Upsampling Via Imposed Edge Statistics, Fattal Single Image Dehazing, Fattal.    Web Page   Demo Code Multiscale Shape and Detail Enhancement from Multi-light Image Collections, Fattal, Agrawala, and Rusinkiewicz Removing Camera Shake from a Single Image, Fergus, Singh, Hertzmann, Roweis, and Freeman Example-Based Super-Resolution, Freeman, Jones, and Pasztor Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake, Harmeling, Hirsch, and Scholkopf Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM, Harmeling, Sra, Hirsch, and Scholkopf Single Image Haze Removal Using Dark Channel Prior, He, Sun, Tang. CVPR 2009. Image Deblurring and Denoising using Color Priors, Joshi, Zitnick, Szeliski, and Kriegman. CVPR 2009. Web Page Image Deblurring using Inertial Measurement Sensors, Joshi, Kang, Zitnick, and Szeliski. SIGGRAPH 2010. Web Page Joint Bilateral Upsampling, Kopf, Cohen, Lischinski, Uyttendaele Blind Deconvolution using a Normalized Sparsity Measure, Krishnan, Tay, and Fergus. CVPR 2011. Web Page Blind Motion Deblurring Using Image Statistics, Levin Image and Depth from a Conventional Camera with a Coded Aperture, Levin, Fergus, Durand, Freeman 
Sparse Deconvolution
 4D Frequency Analysis of Computational Cameras for Depth of Field Extension, Levin, Hasinoff, Green, Durand, and Freeman. SIGGRAPH 2009. Web Page Motion-Invariant Photography, Levin, Sand, Cho, Durand, Freeman. SIGGRAPH 2008. Web Page Noise Estimation from a Single Image, Liu, Freeman, Szeliski, and Kang Image Magnification Using Level-Set Reconstruction, Morse and Schwartzwald Bayesian Image Super-Resolution, Continued, Pickup, Capely, Roberts, and Zisserman Fast Image/Video Upsampling, Shan, Li, Jia, and Tang. Web Page High-quality Motion Deblurring from a Single Image, Shan, Jia, and Argarwala. Web Page Image Super-resolution using Gradient Profile Prior, Sun, Sun, Xu, and Shum. Deblurring Using Regularized Locally-Adaptive Kernel Regression, Takeda, Farsiu, and Milanfar. Web Page Kernel Regression for Image Processing and Reconstruction, Takeda, Farsiu, Milanfar. Web Page Exploiting the Sparse Derivative Prior for Super-Resolution and Image Demosaicing, Tappen, Russell, and Freeman Bayesian Image Super-Resolution, Tipping and Bishop Non-uniform Deblurring for Shaken Images, Whyte, Sivic, Zisserman, and Ponce. CVPR 2010 Deblurring Shaken and Partially Saturated Images, Whyte, Sivic, and Zisserman. ICCP 2012 Image Super-Resolution via Sparse Representation, Yang, Wright, Huang, and Ma 
Image Super-resolution as Sparse Representation of Raw Image Patches Code
 Image Deblurring with Blurred/Noisy Image Pairs, Yuan, Sun, Quan, and Shum Progressive Inter-scale and intra-scale Non-blind Image Deconvolution, Yuan, Sun, Quan, and Shum Denoising vs. Deblurring: HDR Imaging Techniques Using Moving Cameras, Zhang, Deshpande, and Chen. CVPR 2010. Web Page Robust Flash Deblurring, Zhuo and Sim. CVPR 2010. Web Page


Matting and Editing(抠图和图像编辑)

Interactive Digital Photomontage, Agarwala, Dontcheva, Agrawala, Drucker, Colburn, Curless, Salesin, and Cohen Video SnapCut: Robust Video Object Cutout Using Localized Classifiers, Bai, Wang, Simons, and Saprio. SIGGRAPH 2009. Web Page PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing, Barnes, Shechtman, Finkelstein, and Goldman. SIGGRAPH 2009. Web Page Face Swapping: Automatically Replacing Faces in Photographs, Bitouk, Kumar, Dhillon, Belhumeur, and Nayar. SIGGRAPH 2008. Web Page The Patch Transform and Its Applications to Image Editing, Cho, Butman, Avidan, and Freeman. Web Page A Bayesian Approach to Digital Matting, Chuang, Curless, Salesin, and Szeliski
Geodesic Image and Video Editing, Criminisi, Sharp, Rother, and Perez. SIGGRAPH 2011. Coordinates for Instant Image Cloning, Farbman, Hoffer, Lipman, Cohen-Or, and Lischinski. SIGGRAPH 2009. Web Page Shared Sampling for Real-Time Alpha Matting, Gastal and Oliveira. Eurographics 2010. Web Page Geodesic Star Convexity for Interactive Image Segmentation, Gulshan, Rother, Criminisi, Blake, and Zisserman. CVPR 2010. Web Page and Code A Global Sampling Method for Alpha Matting, He, Rhemann, Rother, Tang, Sun. CVPR 2011. Guided Image Filtering, He, Sun, Tang. ECCV 2011. Code Light Mixture Estimation for Spatially Varying White Balance, Hsu, Mertens, Paris, Avidan, and Durand. Web Page Arcimboldo-like Collage Using Internet Images, Huang, Zhang, and Zhang. Web Page Drag-and-Drop Pasting, Jia, Sun, Tang, and Shum. Web Page Exploring Photobios, Kemelmacher-Shlizerman, Shechtman, Garg, Seitz. SIGGRAPH 2011. Web Page Seamless Image Stitching in the Gradient Domain, Levin, Zomet, Peleg, and WeissPhoto Clip Art, Lalonde, Hoiem, Efros, Rother, Winn, and Criminisi A Closed Form Solution to Natural Image Matting, Levin, Lischinski, and Weiss Code
Spectral Matting, Levin, Rav-Acha, and Lischinski Paint Selection, Liu, Sun, and Shum. SIGGRAPH 2009. Poisson Image Editing, Perez, Gangnet, and Blake A Perceptually Motivated Online Benchmark for Image Matting, Rhemann, Rother, Wang, Gelautz, Kohli, and Rott. Web Page
A Spatially Varying PSF-based Prior for Alpha Matting, Rhemann, Rother, Kohli, and Gelautz. CVPR 2010.
 AutoCollage, Rother, Bordeaux, Hamadi, and Blake Alpha Estimation in Natural Images, Ruzon and Tomasi New Appearance Models for Natural Image Matting, Singaraju, Rother, and Rhemann Interactive Editing of Massive Imagery Made Simple: Turning Atlanta into Atlantis, Summa, Scorzelli, Jiang, Bremer, and Pascucci. SIGGRAPH 2011. Web Page Flash Matting, Sun, Li, Kang, and Shum Fast Poisson Blending Using Multi-splines, Szeliski, Uyttendaele, and Steedly. ICCP 2011. Soft Scissors : An Interactive Tool for Realtime High Quality Matting, Wang, Agrawala, and Cohen Image and Video Matting: A Survey, Wang and Cohen

Warping and Morphing(图像扭曲和变形)

As-Rigid-As-Possible Shape Interpolation, Alexa, Cohen-Or, and Levin Feature-Based Image Metamorphosis, Beier and Neely Optimizing Content-Preserving Projections for Wide-Angle Images, Carroll, Agrawala, and Agarwala. SIGGRAPH 2009. Web Page Detail Preserving Shape Deformation in Image Editing, Fang and Hart Feature-Aware Texturing, Gal, Sorkine, and Cohen-Or As-Rigid-As-Possible Shape Manipulation, Igarashi, Moscovich, and Hughes Polymorph: Morphing Among Multiple Images , Lee, Wolberg, and Shin Content-Preserving Warps for 3D Video Stabilization, Liu, Gleicher, Jin and Agarwala. SIGGRAPH 2009. Web Page Moving Gradients: A Path-Based Method for Plausible Image Interpolation, Mahajan, Huang, Matusik, Ramamoorthi, and Belhumeur. SIGGRAPH 2009 Multi-operator Media Retargeting, Rubinstein, Shamir, and Avidan. SIGGRAPH 2009. Web Page Regenerative Morphing, Shechtman, Rav-Acha, Irani, and Seitz. CVPR 2010. Web Page Image Morphing: A Survey , Wolberg

Useful Techniques(其他相关技术)

Gaussian KD-Trees for Fast High-Dimensional Filtering, Adams, Gelfand, Dolson, and Levoy. SIGGRAPH 2009. Web Page Fast High-Dimensional Filtering Using the Permutohedral Lattice, Adams, Baek, and Davis. Eurographics 2010. Web Page Fast Approximate Energy Minimization via Graph Cuts, Boykov, Veksler, and Zabih Edge-Avoiding Wavelets and thier Applications, Fattal Web Page Graphical Models: Probabilistic Inference , Jordan and Weiss Loopy Belief Propagation for Approximate Inference: An Empirical Study , Murphy, Weiss, and Jordan Bilateral Filtering: Papers, Resources, Applications, Paris and Durand Constant time O(1) bilateral filtering Porikli Image Alignment and Stitching: A Tutorial, Szeliski Bilateral Filtering for Gray and Color Images, Tomasi and Manduchi Image Smoothing via L0 Gradient Minimization, Xu, Lu, Xu, and Jia. SIGGRAPH Asia 2011. Web Page Real-Time O(1) Bilateral Filtering, Yang, Tan and Ahuja Source Code SVM for Edge-Preserving Filtering, Yang, Wang and Ahuja

... and Beyond

Photographing long scenes with multi-viewpoint panoramas, Agarwala, Agrawala, Cohen, Salesin, and Szeliski Video Face Replacement, Dale et al. SIGGRAPH ASIA 2011. Web Page Convolution Pyramids, Farbman, Fattal, and Lischinski. SIGGRAPH ASIA 2011. Candid Portrait Selection from Video, Fiss, Argarwala, and Curless. SIGGRAPH ASIA 2011. Web Page Image-Based Rendering Using Image-Based Priors, Fitzgibbon, Wexler, and Zisserman "GrabCut"--Interactive Foreground Extraction using Iterated Graph Cuts, Rother, Kolmogorov, and Blake Web Page Photo Tourism: Exploring Photo Collections in 3D, Snavely, Seitz, and Szeliski Web Page 

Books for General Reference

Digital Image Processing, Second Edition, Gonzalez and Woods Computer Vision: A Modern Approach, Forsyth and Ponce The Art and Science of Digital Compositing, Brinkmann Multiple View Geometry in Computer Vision, Hartley and Zisserman Linear Algebra and Its Applications, Strang Computer Vision: Algorithms and Applications, Richard Szeliski
2017-04-04 17:53:20 houguofei123 阅读数 3554

图像处理研究初学者在看外文文献时,会遇到很多专业术语,为方便理解和学习,现将部分常用图像处理专业术语整理如下:


  • 基本术语
    digital image:数字图像
    digital image processing:数字图像处理
    image digitalization:图像数字化
    image representation:图像表达
    image acquisition:图像的获取
    pixel:像素
    image classification:图像分类
    image transformation:图像变换
    image enhancement:图像增强
    image restoration:图像复原
    image reconstruction:图像重构
    image segmentation:图像分割
    implementation:实现

  • 图像运算
    point operation:点运算
    linear point operation:线性点运算
    non-linear point operation:非线性点运算
    algebra operation:代数运算
    logical operation:逻辑运算
    geometric operation:几何运算
    image translation:图像的平移
    image mirror:图像的镜像
    image rotation:图像的旋转
    image zoom:图像的缩放
    gray resampling:灰度重采样

  • 图像变换
    continuous fourier transform:连续傅里叶变换
    discrete fourier transform:离散傅里叶变换
    fast fourier transform:快速傅里叶变换
    discrete cosine transform:离散余弦变换
    spatial transformation:空间变换

  • 图像增强
    spatial domain:空间域
    frequency domain:频域
    homomorphic filter:同态滤波器

  • 图像复原
    image degradation:图像退化
    noise model:噪声模型
    mean filter:均值滤波器
    order-statistic filter:顺序统计滤波器
    estimation of noise parameter:噪声参数估计
    inverse filter:逆滤波
    minimum meansquare error filter-Wiener filter:最小均方误差滤波-维纳滤波
    geometric distortion correction:几何失真校正

  • 图像压缩编码
    information content:信息量
    information entropy:信息熵
    image data redundancy:图像数据冗余
    fidelity criteria:保真度准则
    coding of lossless image compression:无失真图像
    Huffman coding:哈夫曼编码
    run-length coding:游程编码
    arithmetic coding:算术编码
    lossy image:失真图像
    rate distortion function:率失真函数
    prediction coding:预测编码
    transform coding:变换编码
    vector quantification coding:矢量量化编码
    subband coding:子带编码
    model-based coding:模型基编码
    fractal coding:分形编码

  • 图像分割
    edge detection:边缘检测
    edge connection:边缘连接
    image segmentation using threshold:阈值分割
    global threshold:全局阈值
    adaptive threshold:自适应阈值
    watershed Algorithm:分水岭算法
    region segmentation:区域分割
    region splitting and merging:区域分裂与合并
    binary image processing:二值图像处理
    mathematical morphology image processing:数学形态学图像处理
    open operation and close operation:开运算和闭运算

  • 图像表示与描述
    color feature:颜色特征
    intensity feature:灰度特征
    histogram feature:zhifangtu tezheng
    color moments:颜色矩
    representation of image texture:纹理特征
    autocorrelation function:自相关函数
    statistics of intensity difference:灰度差分统计
    gray-level co-occurrence matrix:灰度共生矩阵
    spectrum feature:频谱特征
    boundary feature:边界特征
    boundary representation:边界表达
    region feature:区域特征
    topological description:拓扑描述
    shape description:形状描述
    moment:矩
    principal components:主成成分
    feature extraction:特征提取
    moving object:运动目标