這一章是計算機視覺部分，主要側重在底層特征提取，視頻分析，跟蹤，目標檢測和識別方面等方面。對于自己不太熟悉的領域比如攝像機標定和立體視覺，僅僅列出上google上引用次數比較多的文獻。有一些剛剛出版的文章，個人非常喜歡，也列出來了。

本章的下載地址：

http://iask.sina.com.cn/u/2252291285/ish?folderid=868772

1. Active Appearance Models

活動表觀模型和活動輪廓模型基本思想來源Snake，現在在人臉三維建模方面得到了很成功的應用，這里列出了三篇最初最經典的文章。對這個領域有興趣的可以從這三篇文章開始入手。

[1998 ECCV] ActiveAppearance Models

[2001 PAMI] ActiveAppearance Models

2. Active Shape Models

[1995 CVIU]Active ShapeModels-Their Training and Application

3. Background modeling andsubtraction

背景建模一直是視頻分析尤其是目標檢測中的一項關鍵技術。雖然最近一直有一些新技術的產生，demo效果也很好，比如基于dynamical texture的方法。但最經典的還是Stauffer等在1999年和2000年提出的GMM方法，他們最大的貢獻在于不用EM去做高斯擬合，而是采用了一種迭代的算法，這樣就不需要保存很多幀的數據，節省了buffer。Zivkovic在2004年的ICPR和PAMI上提出了動態確定高斯數目的方法，把混合高斯模型做到了極致。這種方法效果也很好，而且易于實現。在OpenCV中有現成的函數可以調用。在背景建模大家族里，無參數方法（2000 ECCV）和Vibe方法也值得關注。

[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

詞袋，在這方面暫時沒有什么研究。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的簡稱，是近年來比較受關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

非常不熟悉的領域。僅僅列出了十來篇重要的文獻，供以后學習。

[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

這里面主要來源于圖像檢索，早期的圖像檢測基本基于全局的特征，其中最顯著的就是顏色特征。這一部分可以和前面的Color知識放在一起的。

[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

大紅大熱的DPM，在OpenCV中有一個專門的topic講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

距離變換，在OpenCV中也有實現。用來在二值圖像中尋找種子點非常方便。

[1986 CVGIP] DistanceTransformations in Digital Images

[2008 ACM] 2D EuclideanDistance Transform Algorithms A Comparative Survey

10. Face Detection

最成熟最有名的當屬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，簡單羅列之。

[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

用機器學習的方法來提取角點，號稱很快很好。

[2006 ECCV] Machinelearning for high-speed corner detection

[2010 PAMI] Faster andBetter A Machine Learning Approach to Corner Detection

13. Feature Extraction

這里的特征主要都是各種不變性特征，SIFT，Harris，MSER等也屬于這一類。把它們單獨列出來是因為這些方法更流行一點。關于不變性特征，王永明與王貴錦合著的《圖像局部不變性特征與描述》寫的還不錯。Mikolajczyk在2005年的PAMI上的文章以及2007年的綜述是不錯的學習材料。

[1989 PAMI] On thedetection of dominant points on digital curves

[1997 IJCV] SUSAN—A NewApproach to Low Level Image Processing

[2004 IJCV] MatchingWidely Separated Views Based on Affine Invariant Regions

[2004 IJCV] Scale &Affine Invariant Interest Point Detectors

[2005 PAMI] A performanceevaluation of local descriptors

[2006 IJCV] A Comparisonof Affine Region Detectors

[2007 FAT] Local InvariantFeature Detectors - A Survey

[2011 IJCV] Evaluation ofInterest Point Detectors and Feature Descriptors

14. Feature Matching

[2012 PAMI] LDAHashImproved Matching with Smaller Descriptors

15. Harris

雖然過去了很多年，Harris角點檢測仍然廣泛使用，而且基于它有很多變形。如果仔細看了這種方法，從直觀也可以感覺到這是一種很穩健的方法。

[1988 Harris] A combinedcorner and edge detector

16. Histograms of OrientedGradients

HoG方法也在OpenCV中實現了：HOGDescriptor。

[2005 CVPR] Histograms ofOriented Gradients for Human Detection

NavneetDalalThesis.pdf

17. Image Distance

[1993 PAMI] ComparingImages Using the Hausdorff Distance

18. Image Stitching

圖像拼接，另一個相關的詞是Panoramic。在Computer Vision: Algorithms and Applications一書中，有專門一章是討論這個問題。這里的兩面文章一篇是綜述，一篇是這方面很經典的文章。

[2006 Fnd] Image Alignmentand Stitching A Tutorial

[2007 IJCV] AutomaticPanoramic Image Stitching using Invariant Features

19. KLT

KLT跟蹤算法，基于Lucas-Kanade提出的配準算法。除了三篇很經典的文章，最后一篇給出了OpenCV實現KLT的細節。

[1981] An Iterative ImageRegistration Technique with an Application to Stereo Vision full version

[1994 CVPR] Good Featuresto Track

[2004 IJCV] Lucas-Kanade 20 Years On A Unifying Framework

Pyramidal Implementationof the Lucas Kanade Feature Tracker OpenCV

20. Local Binary Pattern

LBP。OpenCV的Cascade分類器也支持LBP，用來取代Haar特征。

[2002 PAMI]Multiresolution gray-scale and rotation Invariant Texture Classification withLocal Binary Patterns

[2004 ECCV] FaceRecognition with Local Binary Patterns

[2006 PAMI] FaceDescription with Local Binary Patterns

[2011 TIP]Rotation-Invariant Image and Video Description With Local Binary PatternFeatures

21. Low-Level Vision

關于Low level vision的兩篇很不錯的文章

[1998 TIP] A generalframework for low level vision

[2000 IJCV] LearningLow-Level Vision

22. Mean Shift

均值漂移算法，在跟蹤中非常流行的方法。Comaniciu在這個方面做出了重要的貢獻。最后三篇，一篇是CVIU上的top download文章，一篇是最新的PAMI上關于Mean Shift的文章，一篇是OpenCV實現的文章。

[1995 PAMI] Mean shift,mode seeking, and clustering

[2002 PAMI] Mean shift arobust approach toward feature space analysis

[2003 CVPR] Mean-shiftblob tracking through scale space

[2009 CVIU] Objecttracking using SIFT features and mean shift

[2012 PAMI] Mean ShiftTrackers with Cross-Bin Metrics

OpenCV Computer VisionFace Tracking For Use in a Perceptual User Interface

23. MSER

這篇文章發表在2002年的BMVC上，后來直接錄用到2004年的IVC上，內容差不多。MSER在Sonka的書里面也有提到。

[2002 BMVC] Robust WideBaseline Stereo from Maximally Stable Extremal Regions

[2003] MSER AuthorPresentation

[2004 IVC] Robustwide-baseline stereo from maximally stable extremal regions

[2011 PAMI] Are MSERFeatures Really Interesting

24. Object Detection

首先要說的是第一篇文章的作者，Kah-Kay Sung。他是MIT的博士，后來到新加坡國立任教，極具潛力的一個老師。不幸的是，他和他的妻子都在2000年的新加坡空難中遇難，讓人唏噓不已。

http://en.wikipedia.org/wiki/Singapore_Airlines_Flight_006

最后一篇文章也是Fua課題組的，作者給出的demo效果相當好。

[1998 PAMI] Example-basedlearning for view-based human face detection

[2000 CVPR] A Statistical Method for 3D Object Detection Applied to Faces and Cars

[2003 IJCV] Learning theStatistics of People in Images and Video

[2011 PAMI] Learning toDetect a Salient Object

[2012 PAMI] A Real-TimeDeformable Detector

25. Object Tracking

跟蹤也是計算機視覺中的經典問題。粒子濾波，卡爾曼濾波，KLT，mean shift，光流都跟它有關系。這里列出的是傳統意義上的跟蹤，尤其值得一看的是2008的Survey和2003年的Kernel based tracking。

[2003 PAMI] Kernel-basedobject tracking

[2007 PAMI] TrackingPeople by Learning Their Appearance

[2008 ACM] Object TrackingA Survey

[2008 PAMI] Segmentationand Tracking of Multiple Humans in Crowded Environments

[2011 PAMI] Hough Forestsfor Object Detection, Tracking, and Action Recognition

[2011 PAMI] Robust ObjectTracking with Online Multiple Instance Learning

[2012 IJCV] PWP3DReal-Time Segmentation and Tracking of 3D Objects

26. OCR

一個非常成熟的領域，已經很好的商業化了。

[1992 IEEE] Historical reviewof OCR research and development

Video OCR A Survey andPractitioner's Guide

27. Optical Flow

光流法，視頻分析所必需掌握的一種算法。

[1981 AI] DetermineOptical Flow

[1994 IJCV] Performance ofoptical flow techniques

[1995 ACM] The Computationof Optical Flow

[2004 TR] TutorialComputing 2D and 3D Optical Flow

[2005 BOOK] Optical FlowEstimation

[2008 ECCV] LearningOptical Flow

[2011 IJCV] A Database andEvaluation Methodology for Optical Flow

28. Particle Filter

粒子濾波，主要給出的是綜述以及1998 IJCV上的關于粒子濾波發展早期的經典文章。

[1998 IJCV] CONDENSATION—ConditionalDensity Propagation for Visual Tracking

[2002 TSP] A tutorial onparticle filters for online nonlinear non-Gaussian Bayesian tracking

[2002 TSP] Particlefilters for positioning, navigation, and tracking

[2003 SPM] particle filter

29. Pedestrian and Human detection

仍然是綜述類，關于行人和人體的運動檢測和動作識別。

[1999 CVIU] Visualanalysis of human movement_ A survey

[2001 CVIU] A Survey ofComputer Vision-Based Human Motion Capture

[2005 TIP] Image changedetection algorithms a systematic survey

[2006 CVIU] a survey ofavdances in vision based human motion capture

[2007 CVIU] Vision-basedhuman motion analysis An overview

[2007 IJCV] PedestrianDetection via Periodic Motion Analysis

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

[2010 IVC] A survey onvision-based human action recognition

[2012 PAMI] PedestrianDetection An Evaluation of the State of the Art

30. Scene Classification

當相機越來越傻瓜化的時候，自動場景識別就非常重要。這是比拼誰家的Auto功能做的比較好的時候了。

[2001 IJCV] Modeling theShape of the Scene A Holistic Representation of the Spatial Envelope

[2001 PAMI] Visual WordAmbiguity

[2007 PAMI] A ThousandWords in a Scene

[2010 PAMI] EvaluatingColor Descriptors for Object and Scene Recognition

[2011 PAMI] CENTRIST AVisual Descriptor for Scene Categorization

31. Shadow Detection

[2003 PAMI] Detectingmoving shadows-- algorithms and evaluation

32. Shape

關于形狀，主要是兩個方面：形狀的表示和形狀的識別。形狀的表示主要是從邊緣或者區域當中提取不變性特征，用來做檢索或者識別。這方面Sonka的書講的比較系統。2008年的那篇綜述在這方面也講的不錯。至于形狀識別，最牛的當屬J Malik等提出的Shape Context。

[1993 PR] IMPROVED MOMENTINVARIANTS FOR SHAPE DISCRIMINATION

[1993 PR] PatternRecognition by Affine Moment Invariants

[1996 PR] IMAGE RETRIEVALUSING COLOR AND SHAPE

[2001 SMI] Shape matchingsimilarity measures and algorithms

[2002 PAMI] Shape matchingand object recognition using shape contexts

[2004 PR] Review of shaperepresentation and description techniques

[2006 PAMI] IntegralInvariants for Shape Matching

[2008] A Survey of ShapeFeature Extraction Techniques

33. SIFT

關于SIFT，實在不需要介紹太多，一萬多次的引用已經說明問題了。SURF和PCA-SIFT也是屬于這個系列。后面列出了幾篇跟SIFT有關的問題。

[1999 ICCV] Objectrecognition from local scale-invariant features

[2000 IJCV] Evaluation ofInterest Point Detectors

[2003 CVIU] Speeded-UpRobust Features (SURF)

[2004 CVPR] PCA-SIFT AMore Distinctive Representation for Local Image Descriptors

[2004 IJCV] DistinctiveImage Features from Scale-Invariant Keypoints

[2010 IJCV] ImprovingBag-of-Features for Large Scale Image Search

[2011 PAMI] SIFTflow DenseCorrespondence across Scenes and its Applications

34. SLAM

Simultaneous Localization and Mapping, 同步定位與建圖。

SLAM問題可以描述為: 機器人在未知環境中從一個未知位置開始移動,在移動過程中根據位置估計和地圖進行自身定位,同時在自身定位的基礎上建造增量式地圖，實現機器人的自主定位和導航。

[2002 PAMI] SimultaneousLocalization and Map-Building Using Active Vision

[2007 PAMI] MonoSLAMReal-Time Single Camera SLAM

35. Texture Feature

紋理特征也是物體識別和檢索的一個重要特征集。

[1973] Textural featuresfor image classification

[1979 ] Statistical andstructural approaches to texture

[1996 PAMI] Texturefeatures for browsing and retrieval of image data

[2002 PR] Brief review ofinvariant texture analysis methods

[2012 TIP] Color LocalTexture Features for Color Face Recognition

36. TLD

Kadal創立了TLD，跟蹤學習檢測同步進行，達到穩健跟蹤的目的。他的兩個導師也是大名鼎鼎，一個是發明MSER的Matas，一個是Mikolajczyk。他還創立了一個公司TLDVision s.r.o. 這里給出了他的系列文章，最后一篇是剛出來的PAMI。

[2009] Online learning ofrobust object detectors during unstable tracking

[2010 CVPR] P-N LearningBootstrapping Binary Classifiers by Structural Constraints

[2010 ICIP] FACE-TLDTRACKING-LEARNING-DETECTION APPLIED TO FACES

[2012 PAMI]Tracking-Learning-Detection

37. Video Surveillance

前面兩個是兩個很有名的視頻監控系統，里面包含了很豐富的信息量，比如CMU的那個系統里面的背景建模算法也是相當簡單有效的。最后一篇是比較近的綜述。

[2000 CMU TR] A System forVideo Surveillance and Monitoring

[2000 PAMI] W4-- real-timesurveillance of people and their activitie

[2008 MVA] The evolutionof video surveillance an overview

38. Viola-Jones

Haar+Adaboost的弱弱聯手，組成了最強大的利器。在OpenCV里面有它的實現，也可以選擇用LBP來代替Haar特征。

[2001 CVPR] Rapid objectdetection using a boosted cascade of simple features

[2004 IJCV] RobustReal-time Face Detection

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