************************************************************************************************************************************************************************************ 

         在這裡，我特別宣告：本文章的源作者是   楊曉冬  （個人郵箱：[email protected]）。原文的連結是
http://www.iask.sina.com.cn/u/2252291285/ish。版權歸 楊曉冬 朋友所有。

         我非常感謝原作者辛勤地編寫本文章，並願意共享出來。我也希望轉載本文的各位朋友，要註明原作者和出處，以尊重原作者！                 

                                                                                                                                                                             -----------------------------飛信天下

***********************************************************************************************************************************************************************************

                       影象處理與計算機視覺基礎，經典以及最近發展

                                                                                                                             By xdyang（楊曉冬[email protected]）

一、 緒論

1. 為什麼要寫這篇文章

       本文是對現有的影象處理和計算機視覺的經典書籍（後面會有推薦）的一個補充。一般的影象處理書籍都是介紹性的介紹某個方法，在每個領域內都會引用幾十上百篇參考文獻。有時候想深入研究這個領域的時候卻發現文獻太多，不知如何選擇。但實際上在每個領域都有那麼三五篇抑或更多是非讀不可的經典文獻。這些文獻除了提出了很經典的演算法，同時他們的Introduction和Related work也是對所在的領域很好的總結。讀通了這幾篇文獻也就等於深入瞭解了這個領域，比單純的看書收穫要多很多。寫本文的目的就是想把自己所瞭解到的各個領域的經典文章整理出來,不用迷失在參考文獻的汪洋大海里。

2. 影象處理和計算機視覺的分類

B.影象分析：對影象的內容進行分析，提取有意義的特徵，以便於後續的處理。處理的仍然是單幅影象。

C.計算機視覺：對影象分析得到的特徵進行分析，提取場景的語義表示，讓計算機具有人眼和人腦的能力。這時處理的是多幅影象或者序列影象，當然也包括部分單幅影象。

      關於影象處理，影象分析和計算機視覺的劃分並沒有一個很統一的標準。一般的來說，影象處理的書籍總會或多或少的介紹一些影象分析和計算機視覺的知識，比如岡薩雷斯的數字影象處理。而計算機視覺的書籍基本上都會包括影象處理和影象分析，只是不會介紹的太詳細。其實影象處理，影象分析和計算機視覺都可以納入到計算機視覺的範疇：影象處理->低層視覺（low level vision），影象分析->中間層視覺（middle level vision），計算機視覺->高層視覺（high level vision）。這是一般的計算機視覺或者機器視覺的劃分方法。在本文中，仍然按照傳統的方法把這個領域劃分為影象處理，影象分析和計算機視覺。

3. 影象處理和計算機視覺開源庫以及程式語言選擇

（2）OpenCV有一堆影象處理和計算機視覺的大牛在維護，bug在逐步減少，每個新的版本都會帶來不同的驚喜。而且它已經或者逐步在移植到不懂的平臺,並提供了對Python的很好的支援。

（3）在OpenCV上可以嘗試各種最新以及成熟的技術，而不需要自己從頭去寫，比如人臉檢測（Harr，LBP），DPM（Latent SVM），高斯背景模型，特徵檢測，聚類，hough變換等等 。而且它還支援各種機器學習方法（SVM，NN，KNN，決策樹，Boosting等），使用起來很簡單。

（4）文件內容豐富，並且給出了很多示例程式。當然也有一些地方文件描述不清楚，不過看看程式碼就很清楚了。

（5）完全開源。可以從中間提取出任何需要的演算法。

（6）從學校出來後，除極少數會繼續在學術圈裡，大部分還是要進入工業界。現在在工 業界，c/c++仍是主流，很多公司都會優先考慮熟悉或者精通OpenCV的。事實上，在學術界，現在OpenCV也大有取代matlab之勢。以前的demo或者source code，很多作者都願意給出matlab版本的，然後別人再呼哧呼哧改成c版本的。現在作者乾脆給出c/c++版本，或者自己整合到OpenCV中去，這樣能快速提升自己的影響力。
       如果想在影象處理和計算機視覺界有比較深入的研究，並且以後打算進入這個領域工作的話，建議把OpenCV作為自己的主攻方向。如果找工作的時候敢號稱自己精通OpenCV的話，肯定可以找到一份滿意的工作。

4. 本文的特點和結構，以及適合的物件

由於個人精力和視野的關係，有一些我未涉足過的領域不敢斗膽推薦，只是列出了一些引用率比較高的文章，比如攝像機標定和立體視覺。不過將來，由於工作或者其他原因，這些領域也會接觸到，我會逐步增減這些領域的文章。儘管如此，仍然會有疏漏，忘見諒。同時文章的挑選也夾帶了一些個人的喜好，比如我個人比較喜歡low level方向的，尤其是IJCV和PAMI上面的文章，因此這方面也稍微多點，希望不要引起您的反感。如果有什麼意見或者建議，歡迎mail我。文章和資源我都會在我的csdn blog和sina ishare同步更新。此申明：這些論文的版權歸作者及其出版商所有，請勿用於商業目的。

      本文的安排如下。第一部分是緒論。第二部分是影象處理中所需要用到的理論基礎，主要是這個領域所涉及到的一些比較好的參考書籍。第三部分是計算機視覺中所涉及到的訊號處理和模式識別文章。由於影象處理與影象分析太難區分了，第四部分集中討論了它們。第五部分是計算機視覺部分。最後是小結。

二、 影象處理與計算機視覺相關的書籍

1. 數學

2. 訊號處理

2.1經典訊號處理

訊號與系統(第2版) Alan V.Oppenheim等著 劉樹棠譯

離散時間訊號處理(第2版) A.V.奧本海姆等著 劉樹棠譯

數字訊號處理:理論演算法與實現 胡廣書 (編者)

2.2隨機訊號處理

現代訊號處理 張賢達著

統計訊號處理基礎:估計與檢測理論 Steven M.Kay等著 羅鵬飛等譯

自適應濾波器原理(第4版) Simon Haykin著 鄭寶玉等譯

2.3 小波變換

訊號處理的小波導引:稀疏方法(原書第3版) tephane Malla著, 戴道清等譯

2.4 資訊理論

資訊理論基礎(原書第2版) Thomas M.Cover等著 阮吉壽等譯

3. 模式識別

Pattern Recognition and Machine Learning Bishop, Christopher M. Springer

模式識別(英文版)(第4版) 西奧多裡德斯著

Pattern Classification (2nd Edition) Richard O. Duda等著

Statistical Pattern Recognition, 3rd Edition Andrew R. Webb等著

模式識別(第3版) 張學工著

4. 影象處理與計算機視覺的書籍推薦

影象處理，分析與機器視覺 第三版 Sonka等著 艾海舟等譯

Image Processing, Analysis and Machine Vision

                ( 附：這本書是影象處理與計算機視覺裡面比較全的一本書了，幾乎涵蓋了影象視覺領域的各個方面。中文版的個人感覺也還可以，值得一看。)

數字影象處理 第三版 岡薩雷斯等著

Digital Image Processing

(附：數字影象處理永遠的經典，現在已經出到了第三版，相當給力。我的導師曾經說過，這本書寫的很優美，對寫英文論文也很有幫助，建議購買英文版的。)

計算機視覺：理論與演算法 Richard Szeliski著

Computer Vision: Theory and Algorithm

                (附：微軟的Szeliski寫的一本最新的計算機視覺著作。內容非常豐富，尤其包括了作者的研究興趣，比如一般的書裡面都沒有的Image Stitching和                       Image Matting等。這也從另一個側面說明這本書的通用性不如Sonka的那本。不過作者開放了這本書的電子版，可以有選擇性的閱讀。
                  http://szeliski.org/Book/
                  Multiple View Geometry in Computer Vision 第二版Harley等著
                 引用達一萬多次的經典書籍了。第二版到處都有電子版的。第一版曾出過中文版的，後來絕版了。網上也可以找到中英文版的電子版。)

計算機視覺：一種現代方法 DA Forsyth等著

Computer Vision: A Modern Approach

MIT的經典教材。雖然已經過去十年了，還是值得一讀。期待第二版

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

(附：為數不多的英國人寫的書，偏向於工業應用。)

數字影象處理 第四版 Pratt著

Digital Image Processing

(附：寫作風格獨樹一幟，也是影象處理領域很不錯的一本書。網上也可以找到非常清晰的電子版。)

5. 小結

三、 計算機視覺中的訊號處理與模式識別

1. Boosting

[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 Object Detection

[2003] The Boosting Approach to Machine Learning An Overview

[2004 IJCV] Robust Real-time Face Detection

2. Clustering

[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

[2006 TIT] Compressed Sensing

[2008 SPM] An Introduction to Compressive Sampling

[2011 TSP] Structured Compressed Sensing From Theory to Applications

4. Decision Trees

[1986] Introduction to Decision Trees

5. Dynamical Programming

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

[Book Chapter] Dynamic Programming

6. Expectation Maximization

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

[1996 SPM] The Expectation-Maximzation Algorithm

7. Graphical Models

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

8. Hidden Markov Model

[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 hidden Markov models

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

Hmm Chinese book.pdf

9. Independent Component Analysis

[1999] Independent Component Analysis A Tutorial

[2000 NN] Independent component analysis algorithms and applications

[2000] Independent Component Analysis Algorithms and Applications

10. Information Theory

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

[2010] An information theory perspective on computational vision

11. Kalman Filter

[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

[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

[2001 PAMI] PCA versus LDA

[2001] Nonlinear component analysis as a kernel eigenvalue problem

[2002] A Tutorial on Principal Component Analysis

[2009] A Tutorial on Principal Component Analysis

[2011] Robust Principal Component Analysis

[Book Chapter] Singular Value Decomposition and Principal Component Analysis

14. Random Forest

[2001 ML] Random Forests

15. RANSAC

[2009 BMVC] Performance Evaluation of RANSAC Family

16. Singular Value Decomposition

[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

[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

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

[1996 PAMI] Image Representation using 2D Gabor Wavelet

[1998 ] FACTORING WAVELET TRANSFORMS INTO 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 image contrast 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

四、 影象處理與分析

1. Bilateral Filter

[1998 ICCV] Bilateral Filtering for Gray and Color Images

[2008 TIP] Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal

2. Color

[1991 IJCV] Color Indexing

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

[2001 PAMI] Color invariance

[2002 IJCV] Statistical Color Models with Application to Skin Detection

[2003] A review of RGB color spaces

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

Gamma.pdf

GammaFAQ.pdf

3. Compression and Encoding

[2005 IEEE] Trends and perspectives in image and video coding

4. Contrast Enhancement

[2002 IJCV] Vision and the Atmosphere

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

[2006 TIP] Gray-level grouping (GLG) an automatic method for optimized image contrast enhancement-part II

[2006 TIP] Gray-level grouping (GLG) an automatic method for optimized image contrast Enhancement-part I

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

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

5. Deblur (Restoration)

[1972] Bayesian-Based Iterative Method of Image Restoration

[1974] an iterative technique for the rectification of observed distributions

[1990 IEEE] Iterative methods for image deblurring

[1996 SPM] Blind Image Deconvolution

[1997 SPM] Digital image restoration

[2005] Digital Image Reconstruction - Deblurring and Denoising

[2006 Siggraph] Removing Camera 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

[2008 Siggraph] Single Image Dehazing

[2009 CVPR] Single Image Haze Removal Using Dark Channel Prior

[2011 PAMI] Single Image Haze Removal Using Dark Channel Prior

7. Denoising

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

[1992 SIAM] Image selective smoothing and edge detection by nonlinear diffusion

[1992] Nonlinear total variation based noise removal algorithms

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

[1995 TIT] De-noising by soft-thresholding

[1998 TIP] Orientation diffusions

[2000 TIP] Adaptive wavelet thresholding for image denoising and compression

[2000 TIP] Fourth-order partial differential equations for noise removal

[2001] Denoising through wavelet shrinkage

[2002 TIP] The Curvelet Transform for Image Denoising

[2003 TIP] Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time

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

[2009 TIP] Is Denoising Dead

8. Edge Detection

[1980] theory of edge detection

[1983 Canny Thesis] find edge

[1986 PAMI] A Computational Approach to Edge Detection

[1990 PAMI] Scale-space and edge detection using anisotropic diffusion

[1991 PAMI] The design and use of steerable filters

[1995 PR] Multiresolution edge detection techniques

[1996 TIP] Optimal edge detection in two-dimensional images

[1998 PAMI] Local Scale Control for Edge Detection and Blur Estimation

[2003 PAMI] Statistical edge detection_ learning and evaluating edge cues

[2004 IEEE] Edge Detection Revisited

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

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

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

9. Graph Cut

[2000 PAMI] Normalized cuts and image segmentation

[2001 PAMI] Fast approximate energy minimization via graph cuts

[2004 PAMI] What energy functions can be minimized via graph cuts

10. Hough Transform

[1986 CVGIU] A Survey of the Hough Transform

[1989] A Comparative study of Hough transform methods for circle finding

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

[1997 PR] Extraction of line features in a noisy image

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

11. Image Interpolation

[2000 TMI] Interpolation revisited

12. Image Matting

[2008 Fnd] Image and Video Matting A Survey

[2008 PAMI] A Closed-Form Solution to Natural Image Matting

[2008 PAMI] Spectral Matting

13. Image Modeling

[1994] The statistics of natural images

[2003 JMIV] On Advances in Statistical Modeling of Natural Images

[2009 IJCV] Fields of Experts

[2009 PAMI] Modeling multiscale subbands of photographic images with fields of Gaussian scale mixtures

14. Image Quality Assessment

[2004 TIP] Image quality assessment from error visibility to structural similarity

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

15. Image Registration

[1992 MIA] Image matching as a diffusion process

[1992 PAMI] A Method for Registration of 3-D shapes

[1992] a survey of image registration techniques

[1998 MIA] A survey of medical image registration

[2003 IVC] Image registration methods a survey

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

[2011 TIP] Hairis registration

16. Image Retrieval

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

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

[2002] Content-Based Image Retrieval Systems A Survey

[2008] Content-Based Image Retrieval-Literature Survey

[2010] Plant Image Retrieval Using Color,Shape and Texture Features

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

CBIR Chinese

fundament of cbir

17. Image Segmentation

[2004 IJCV] Efficient Graph-Based Image Segmentation

[2008 CVIU] Image segmentation evaluation A survey of unsupervised methods

[2011 PAMI] Contour Detection and Hierarchical Image Segmentation

18. Level Set

[1995 PAMI] Shape modeling with front propagation_ a level set approach

[2001 JCP] Level Set Methods_ An Overview and Some Recent Results

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

[2007 IJCV] A Review of Statistical Approaches to Level Set Segmentation

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

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

19. Pyramid

[1983] The Laplacian Pyramid as a Compact Image Code

20. Radon Transform

[1993 PAMI] Image representation via a finite Radon transform

[1993 TIP] The fast discrete radon transform I theory

[2007 IVC] Generalised finite radon transform for N×N images

21. Scale Space