[1996 SPM] The Expectation-Maximzation Algorithm
7. Graphical Models 伯克利的乔丹大师的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 hidden Markov 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 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 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Book Chapter[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