图像处理与计算机视觉：基础，经典以及最近发展（5）计算机视觉

清疚 2022-09-17 15:29 157阅读 0赞

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：

[http://iask.sina.com.cn/u/2252291285/ish?folderid=868772][http_iask.sina.com.cn_u_2252291285_ish_folderid_868772]

## 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 ##

这里的特征主要都是各种不变性特征，SIFT，Harris，MSER等也属于这一类。把它们单独列出来是因为这些方法更流行一点。关于不变性特征，王永明与王贵锦合著的《图像局部不变性特征与描述》写的还不错。Mikolajczyk在2005年的PAMI上的文章以及2007年的综述是不错的学习材料。

\[1989 PAMI\] 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 ##

虽然过去了很多年，Harris角点检测仍然广泛使用，而且基于它有很多变形。如果仔细看了这种方法，从直观也可以感觉到这是一种很稳健的方法。

\[1988 Harris\] A combinedcorner and edge detector

## 16. Histograms of OrientedGradients ##

HoG方法也在OpenCV中实现了：HOGDescriptor。

\[2005 CVPR\] Histograms ofOriented Gradients for Human Detection

NavneetDalalThesis.pdf

## 17. Image Distance ##

\[1993 PAMI\] ComparingImages Using the Hausdorff Distance

## 18. Image Stitching ##

图像拼接，另一个相关的词是Panoramic。在Computer Vision: Algorithms and Applications一书中，有专门一章是讨论这个问题。这里的两面文章一篇是综述，一篇是这方面很经典的文章。

\[2006 Fnd\] Image Alignmentand Stitching A Tutorial

\[2007 IJCV\] AutomaticPanoramic Image Stitching using Invariant Features

## 19. KLT ##

KLT跟踪算法，基于Lucas-Kanade提出的配准算法。除了三篇很经典的文章，最后一篇给出了OpenCV实现KLT的细节。

\[1981\] 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分类器也支持LBP，用来取代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 ##

关于Low level vision的两篇很不错的文章

\[1998 TIP\] A generalframework for low level vision

\[2000 IJCV\] LearningLow-Level Vision

## 22. Mean Shift ##

均值漂移算法，在跟踪中非常流行的方法。Comaniciu在这个方面做出了重要的贡献。最后三篇，一篇是CVIU上的top download文章，一篇是最新的PAMI上关于Mean Shift的文章，一篇是OpenCV实现的文章。

\[1995 PAMI\] 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 ##

这篇文章发表在2002年的BMVC上，后来直接录用到2004年的IVC上，内容差不多。MSER在Sonka的书里面也有提到。

\[2002 BMVC\] 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 ##

首先要说的是第一篇文章的作者，Kah-Kay Sung。他是MIT的博士，后来到新加坡国立任教，极具潜力的一个老师。不幸的是，他和他的妻子都在2000年的新加坡空难中遇难，让人唏嘘不已。

[http://en.wikipedia.org/wiki/Singapore\_Airlines\_Flight\_006][http_en.wikipedia.org_wiki_Singapore_Airlines_Flight_006]

最后一篇文章也是Fua课题组的，作者给出的demo效果相当好。

\[1998 PAMI\] 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 ##

跟踪也是计算机视觉中的经典问题。粒子滤波，卡尔曼滤波，KLT，mean shift，光流都跟它有关系。这里列出的是传统意义上的跟踪，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 ##

一个非常成熟的领域，已经很好的商业化了。

\[1992 IEEE\] Historical reviewof OCR research and development

Video OCR A Survey andPractitioner's Guide

## 27. Optical Flow ##

光流法，视频分析所必需掌握的一种算法。

\[1981 AI\] 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 ##

粒子滤波，主要给出的是综述以及1998 IJCV上的关于粒子滤波发展早期的经典文章。

\[1998 IJCV\] 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 ##

仍然是综述类，关于行人和人体的运动检测和动作识别。

\[1999 CVIU\] 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 ##

当相机越来越傻瓜化的时候，自动场景识别就非常重要。这是比拼谁家的Auto功能做的比较好的时候了。

\[2001 IJCV\] 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 ##

关于形状，主要是两个方面：形状的表示和形状的识别。形状的表示主要是从边缘或者区域当中提取不变性特征，用来做检索或者识别。这方面Sonka的书讲的比较系统。2008年的那篇综述在这方面也讲的不错。至于形状识别，最牛的当属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 ##

关于SIFT，实在不需要介绍太多，一万多次的引用已经说明问题了。SURF和PCA-SIFT也是属于这个系列。后面列出了几篇跟SIFT有关的问题。

\[1999 ICCV\] 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, 同步定位与建图。

SLAM问题可以描述为: 机器人在未知环境中从一个未知位置开始移动,在移动过程中根据位置估计和地图进行自身定位,同时在自身定位的基础上建造增量式地图，实现机器人的自主定位和导航。

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

\[2007 PAMI\] MonoSLAMReal-Time Single Camera SLAM

## 35. Texture Feature ##

纹理特征也是物体识别和检索的一个重要特征集。

\[1973\] 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创立了TLD，跟踪学习检测同步进行，达到稳健跟踪的目的。他的两个导师也是大名鼎鼎，一个是发明MSER的Matas，一个是Mikolajczyk。他还创立了一个公司[TLDVision s.r.o][]. 这里给出了他的系列文章，最后一篇是刚出来的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 ##

前面两个是两个很有名的视频监控系统，里面包含了很丰富的信息量，比如CMU的那个系统里面的背景建模算法也是相当简单有效的。最后一篇是比较近的综述。

\[2000 CMU TR\] 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的弱弱联手，组成了最强大的利器。在OpenCV里面有它的实现，也可以选择用LBP来代替Haar特征。

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

\[2004 IJCV\] RobustReal-time Face Detection

[http_iask.sina.com.cn_u_2252291285_ish_folderid_868772]: http://iask.sina.com.cn/u/2252291285/ish?folderid=868772
[http_en.wikipedia.org_wiki_Singapore_Airlines_Flight_006]: http://en.wikipedia.org/wiki/Singapore_Airlines_Flight_006
[TLDVision s.r.o]: http://tldvision.com/