Last update: 2012-6-7
这一章是计算机视觉部分，主要侧重在底层特征提取，视频分析，跟踪，目标检测和识别方面等方面。对于自己不太熟悉的领域比如摄像机标定和立体视觉，仅仅列出上google上引用次数比较多的文献。有一些刚刚出版的文章，个人非常喜欢，也列出来了。
本章的下载地址：
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1. Active Appearance Models
活动表观模型和活动轮廓模型基本思想来源Snake，现在在人脸三维建模方面得到了很成功的应用，这里列出了三篇最初最经典的文章。对这个领域有兴趣的可以从这三篇文章开始入手。
[1998 ECCV] ActiveAppearance Models
[2001 PAMI] ActiveAppearance Models
2. Active Shape Models
[1995 CVIU]Active ShapeModels-Their Training and Application
3. Background modeling andsubtraction
背景建模一直是视频分析尤其是目标检测中的一项关键技术。虽然最近一直有一些新技术的产生，demo效果也很好，比如基于dynamical texture的方法。但最经典的还是Stauffer等在1999年和2000年提出的GMM方法，他们最大的贡献在于不用EM去做高斯拟合，而是采用了一种迭代的算法，这样就不需要保存很多帧的数据，节省了buffer。Zivkovic在2004年的ICPR和PAMI上提出了动态确定高斯数目的方法，把混合高斯模型做到了极致。这种方法效果也很好，而且易于实现。在OpenCV中有现成的函数可以调用。在背景建模大家族里，无参数方法（2000 ECCV）和Vibe方法也值得关注。
[1997 PAMI] PfinderReal-Time Tracking of the Human Body
[1999 CVPR] Adaptivebackground mixture models for real-time tracking
[1999 ICCV] WallflowerPrinciples and Practice of Background Maintenance
[2000 ECCV] Non-parametricModel for Background Subtraction
[2000 PAMI] LearningPatterns of Activity Using Real-Time Tracking
[2002 PIEEE] Backgroundand foreground modeling using nonparametric kernel density estimation forvisual surveillance
[2004 ICPR] Improvedadaptive Gaussian mixture model for background subtraction
[2004 PAMI] Recursiveunsupervised learning of finite mixture models
[2006 PRL] Efficientadaptive density estimation per image pixel for the task of backgroundsubtraction
[2011 TIP] ViBe AUniversal Background Subtraction Algorithm for Video Sequences
4. Bag of Words
词袋，在这方面暂时没有什么研究。列出三篇引用率很高的文章，以后逐步解剖之。
[2003 ICCV] Video Google AText Retrieval Approach to Object Matching in Videos
[2004 ECCV] VisualCategorization with Bags of Keypoints
[2006 CVPR] Beyond bags offeatures Spatial pyramid matching for recognizing natural scene categories
5. BRIEF
BRIEF是BinaryRobust Independent Elementary Features的简称，是近年来比较受关注的特征描述的方法。ORB也是基于BRIEF的。
[2010 ECCV] BRIEF BinaryRobust Independent Elementary Features
[2011 ICCV] ORB anefficient alternative to SIFT or SURF
[2012 PAMI] BRIEFComputing a Local Binary Descriptor Very Fast
6. Camera Calibration and StereoVision
非常不熟悉的领域。仅仅列出了十来篇重要的文献，供以后学习。
[1979 Marr] AComputational Theory of Human Stereo Vision
[1985] Computationalvision and regularization theory
[1987 IEEE] A versatilecamera calibration technique for high-accuracy 3D machine vision metrologyusing off-the-shelf TV cameras and lenses
[1987] ProbabilisticSolution of Ill-Posed Problems in Computational Vision
[1988 PIEEE] Ill-PosedProblems in Early Vision
[1989 IJCV] KalmanFilter-based Algorithms for Estimating Depth from Image Sequences
[1990 IJCV] RelativeOrientation
[1990 IJCV] Usingvanishing points for camera calibration
[1992 ECCV] Cameraself-calibration Theory and experiments
[1992 IJCV] A theory ofself-calibration of a moving camera
[1992 PAMI] Cameracalibration with distortion models and accuracy evaluation
[1994 IJCV] TheFundamental Matrix Theory, Algorithms, and Stability Analysis
[1994 PAMI] a stereomatching algorithm with an adaptive window theory and experiment
[1999 ICCV] Flexiblecamera calibration by viewing a plane from unknown orientations
[1999 IWAR] Markertracking and hmd calibration for a video-based augmented reality conferencingsystem
[2000 PAMI] A flexible newtechnique for camera calibration
7. Color and Histogram Feature
这里面主要来源于图像检索，早期的图像检测基本基于全局的特征，其中最显著的就是颜色特征。这一部分可以和前面的Color知识放在一起的。
[1995 SPIE] Similarity ofcolor images
[1996 PR] IMAGE RETRIEVALUSING COLOR AND SHAPE
[1996] comparing imagesusing color coherence vectors
[1997 ] Image IndexingUsing Color Correlograms
[2001 TIP] An EfficientColor Representation for Image Retrieval
[2009 CVIU] Performanceevaluation of local colour invariants
8. Deformable Part Model
大红大热的DPM，在OpenCV中有一个专门的topic讲DPM和latent svm
[2008 CVPR] ADiscriminatively Trained, Multiscale, Deformable Part Model
[2010 CVPR] Cascade ObjectDetection with Deformable Part Models
[2010 PAMI] ObjectDetection with Discriminatively Trained Part-Based Models
9. Distance Transformations
距离变换，在OpenCV中也有实现。用来在二值图像中寻找种子点非常方便。
[1986 CVGIP] DistanceTransformations in Digital Images
[2008 ACM] 2D EuclideanDistance Transform Algorithms A Comparative Survey
10. Face Detection
最成熟最有名的当属Haar+Adaboost
[1998 PAMI] NeuralNetwork-Based Face Detection
[2002 PAMI] Detectingfaces in images a survey
[2002 PAMI] Face Detectionin Color Images
[2004 IJCV] RobustReal-Time Face Detection
11. Face Recognition
不熟悉，简单罗列之。
[1991] Face RecognitionUsing Eigenfaces
[2000 PAMI] AutomaticAnalysis of Facial Expressions The State of the Art
[2000] Face Recognition ALiterature Survey
[2006 PR] Face recognitionfrom a single image per person A survey
[2009 PAMI] Robust FaceRecognition via Sparse Representation
12. FAST
用机器学习的方法来提取角点，号称很快很好。
[2006 ECCV] Machinelearning for high-speed corner detection
[2010 PAMI] Faster andBetter A Machine Learning Approach to Corner Detection
13. Feature Extraction一个今天胜过两个明天
