图像处理与计算机视觉基础，经典以及最近发展(二)

11. Image Interpolation

图像插值，偶尔也用得上。一般来说，双三次也就够了

[2000 TMI] Interpolation revisited

12.
Image Matting

也就是最近，我才知道这个词翻译成中文是抠图，比较难听，不知道是谁开始这么翻译的。没有研究，请看文章以及Richard Szeliski的相关章节。以色列美女Levin在这方面有两篇PAMI。

[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

图像的统计模型。这方面有一本专门的著作Natural Image Statistics

[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

在图像质量评价方面，Bovik是首屈一指的。这位老师也很有意思，作为编辑出版了很多书。他也是IEEE的Fellow

[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

图像配准最早的应用在医学图像上，在图像融合之前需要对图像进行配准。在现在的计算机视觉中，配准也是一个需要理解的概念，比如跟踪，拼接等。在KLT中，也会涉及到配准。这里主要是综述文献。

[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

图像检索曾经很热，在2000年之后似乎消停了一段时间。最近各种图像的不变性特征提出来之后，再加上互联网搜索的商业需求，这个方向似乎又要火起来了，尤其是在商业界，比如淘淘搜。这仍然是一个非常值得关注的方面。而且图像检索与目标识别具有相通之处，比如特征提取和特征降维。这方面的文章值得一读。在最后给出了两篇Book
chapter，其中一篇还是中文的。

[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

图像分割，非常基本但又非常难的一个问题。建议看Sonka和冈萨雷斯的书。这里给出几篇比较好的文章，再次看到了J Malik。他们给出了源代码和测试集，有兴趣的话可以试试。

[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

大名鼎鼎的水平集，解决了Snake固有的缺点。Level set的两位提出者Sethian和Osher最后反目，实在让人遗憾。个人以为，这种方法除了迭代比较费时，在真实场景中的表现让人生疑。不过，2008年ECCV上的PWP方法在结果上很吸引人。在重初始化方面，Chunming
Li给出了比较好的解决方案

[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

其实小波变换就是一种金字塔分解算法，而且具有无失真重构和非冗余的优点。Adelson在1983年提出的Pyramid优点是比较简单，实现起来比较方便。

[1983] The Laplacian Pyramid as a Compact Image Code

20.
Radon Transform

Radon变换也是一种很重要的变换，它构成了图像重建的基础。关于图像重建和radon变换，可以参考章毓晋老师的书，讲的比较清楚。

[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

尺度空间滤波在现代不变特征中是一个非常重要的概念，有人说SIFT的提出者Lowe是不变特征之父，而Linderburg是不变特征之母。虽然尺度空间滤波是Witkin最早提出的，但其理论体系的完善和应用还是Linderburg的功劳。其在1998年IJCV上的两篇文章值得一读，不管是特征提取方面还是边缘检测方面。

[1987] Scale-space filtering

[1990 PAMI] Scale-Space for Discrete Signals

[1994] Scale-space theory A basic tool for analysing structures at different scales

[1998 IJCV] Edge Detection and Ridge Detection with Automatic Scale Selection

[1998 IJCV] Feature Detection with Automatic Scale Selection

22.
Snake

活动轮廓模型，改变了传统的图像分割的方法，用能量收缩的方法得到一个统计意义上的能量最小（最大）的边缘。

[1987 IJCV] Snakes Active Contour Models

[1996 ] deformable model in medical image A Survey

[1997 IJCV] geodesic active contour

[1998 TIP] Snakes, shapes, and gradient vector flow

[2000 PAMI] Geodesic active contours and level sets for the detection and tracking of moving objects

[2001 TIP] Active contours without edges

23.
Super Resolution

超分辨率分析。对这个方向没有研究，简单列几篇文章。其中Yang Jianchao的那篇在IEEE上的下载率一直居高不下。

[2002] Example-Based Super-Resolution

[2009 ICCV] Super-Resolution from a Single Image

[2010 TIP] Image Super-Resolution Via Sparse Representation

24.
Thresholding

阈值分割是一种简单有效的图像分割算法。这个topic在冈萨雷斯的书里面讲的比较多。这里列出OTSU的原始文章以及一篇不错的综述。

[1979 IEEE] OTSU A threshold selection method from gray-level histograms

[2001 JISE] A Fast Algorithm for Multilevel Thresholding

[2004 JEI] Survey over image thresholding techniques and quantitative performance evaluation

25.
Watershed

分水岭算法是一种非常有效的图像分割算法，它克服了传统的阈值分割方法的缺点，尤其是Marker-Controlled Watershed，值得关注。Watershed在冈萨雷斯的书里面讲的比较详细。

[1991 PAMI] Watersheds in digital spaces an efficient algorithm based on immersion simulations

[2001]The Watershed Transform Definitions, Algorithms and Parallelizat on Strategies

五、
计算机视觉

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。

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，这里列出了三篇最早最经典的文章。对这个领域有兴趣的可以从这三篇文章开始入手。

[1998 ECCV] Active Appearance Models

[2001 PAMI] Active Appearance Models

2.
Active Shape Models

[1995 CVIU]Active Shape Models-Their Training and Application

3.
Background modeling and subtraction

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] Pfinder Real-Time Tracking of the Human Body

[1999 CVPR] Adaptive background mixture models for real-time tracking

[1999 ICCV] Wallflower Principles and Practice of Background Maintenance

[2000 ECCV] Non-parametric Model for Background Subtraction

[2000 PAMI] Learning Patterns of Activity Using Real-Time Tracking

[2002 PIEEE] Background and foreground modeling using nonparametric

kernel density estimation for visual surveillance

[2004 ICPR] Improved adaptive Gaussian mixture model for background subtraction

[2004 PAMI] Recursive unsupervised learning of finite mixture models

[2006 PRL] Efficient adaptive density estimation per image pixel for the task of background subtraction

[2011 TIP] ViBe A Universal 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 A Text Retrieval Approach to Object Matching in Videos

[2004 ECCV] Visual Categorization with Bags of Keypoints

[2006 CVPR] Beyond bags of features Spatial pyramid matching for recognizing natural scene categories

5.
BRIEF

BRIEF是Binary Robust 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 Binary Robust Independent Elementary Features

[2011 ICCV] ORB an efficient alternative to SIFT or SURF

[2012 PAMI] BRIEF Computing a Local Binary Descriptor Very Fast

6.
Camera Calibration and Stereo Vision

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] A Computational Theory of Human Stereo Vision

[1985] Computational vision and regularization theory

[1987 IEEE] A versatile camera calibration technique for

high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses

[1987] Probabilistic Solution of Ill-Posed Problems in Computational Vision

[1988 PIEEE] Ill-Posed Problems in Early Vision

[1989 IJCV] Kalman Filter-based Algorithms for Estimating Depth from Image Sequences

[1990 IJCV] Relative Orientation

[1990 IJCV] Using vanishing points for camera calibration

[1992 ECCV] Camera self-calibration Theory and experiments

[1992 IJCV] A theory of self-calibration of a moving camera

[1992 PAMI] Camera calibration with distortion models and accuracy evaluation

[1994 IJCV] The Fundamental Matrix Theory, Algorithms, and Stability Analysis

[1994 PAMI] a stereo matching algorithm with an adaptive window theory and experiment

[1999 ICCV] Flexible camera calibration by viewing a plane from unknown orientations

[1999 IWAR] Marker tracking and hmd calibration for a video-based augmented reality conferencing system

[2000 PAMI] A flexible new technique 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 of color images

[1996 PR] IMAGE RETRIEVAL USING COLOR AND SHAPE

[1996] comparing images using color coherence vectors

[1997 ] Image Indexing Using Color Correlograms

[2001 TIP] An Efficient Color Representation for Image Retrieval

[2009 CVIU] Performance evaluation 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] A Discriminatively Trained, Multiscale, Deformable Part Model

[2010 CVPR] Cascade Object Detection with Deformable Part Models

[2010 PAMI] Object Detection 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] Distance Transformations in Digital Images

[2008 ACM] 2D Euclidean Distance Transform Algorithms A Comparative Survey

10.
Face Detection

最成熟最有名的当属Haar+Adaboost

[1998 PAMI] Neural Network-Based Face Detection

[2002 PAMI] Detecting faces in images a survey

[2002 PAMI] Face Detection in Color Images

[2004 IJCV] Robust Real-Time Face Detection

11.
Face Recognition

不熟悉，简单罗列之。

[1991] Face Recognition Using Eigenfaces

[2000 PAMI] Automatic Analysis of Facial Expressions The State of the Art

[2000] Face Recognition A Literature Survey

[2006 PR] Face recognition from a single image per person A survey

[2009 PAMI] Robust Face Recognition 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] Machine learning for high-speed corner detection

[2010 PAMI] Faster and Better 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 the detection of dominant points on digital curves

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

[2004 IJCV] Matching Widely Separated Views Based on Affine Invariant Regions

[2004 IJCV] Scale & Affine Invariant Interest Point Detectors

[2005 PAMI] A performance evaluation of local descriptors

[2006 IJCV] A Comparison of Affine Region Detectors

[2007 FAT] Local Invariant Feature Detectors - A Survey

[2011 IJCV] Evaluation of Interest Point Detectors and Feature Descriptors

14.
Feature Matching

Fua课题组在今年PAMI上的一篇文章，感觉还不错

[2012 PAMI] LDAHash Improved 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 combined corner and edge detector

16.
Histograms of Oriented Gradients

HoG方法也在OpenCV中实现了：HOGDescriptor。

[2005 CVPR] Histograms of Oriented Gradients for Human Detection

NavneetDalalThesis.pdf

17.
Image Distance

[1993 PAMI] Comparing Images 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 Alignment and Stitching A Tutorial

[2007 IJCV] Automatic Panoramic 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 Image Registration Technique with an Application to Stereo Vision full version

[1994 CVPR] Good Features to Track

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

Pyramidal Implementation of 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 with Local Binary Patterns

[2004 ECCV] Face Recognition with Local Binary Patterns

[2006 PAMI] Face Description with Local Binary Patterns

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

21.
Low-Level Vision

关于Low level vision的两篇很不错的文章

[1998 TIP] A general framework for low level vision

[2000 IJCV] Learning Low-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 a robust approach toward feature space analysis

[2003 CVPR] Mean-shift blob tracking through scale space

[2009 CVIU] Object tracking using SIFT features and mean shift

[2012 PAMI] Mean Shift Trackers with Cross-Bin Metrics

OpenCV Computer Vision Face 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 Wide Baseline Stereo from Maximally Stable Extremal Regions

[2003] MSER Author Presentation

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

[2011 PAMI] Are MSER Features 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
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-based learning for view-based human face detection

[2003 IJCV] Learning the Statistics of People in Images and Video

[2011 PAMI] Learning to Detect a Salient Object

[2012 PAMI] A Real-Time Deformable 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-based object tracking

[2007 PAMI] Tracking People by Learning Their Appearance

[2008 ACM] Object Tracking A Survey

[2008 PAMI] Segmentation and Tracking of Multiple Humans in Crowded Environments

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

[2011 PAMI] Robust Object Tracking with Online Multiple Instance Learning

[2012 IJCV] PWP3D Real-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 review of OCR research and development

Video OCR A Survey and Practitioner's Guide

27.
Optical Flow

光流法，视频分析所必需掌握的一种算法。

[1981 AI] Determine Optical Flow

[1994 IJCV] Performance of optical flow techniques

[1995 ACM] The Computation of Optical Flow

[2004 TR] Tutorial Computing 2D and 3D Optical Flow

[2005 BOOK] Optical Flow Estimation

[2008 ECCV] Learning Optical Flow

[2011 IJCV] A Database and Evaluation 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—Conditional Density Propagation for Visual Tracking

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

[2002 TSP] Particle filters 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] Visual analysis of human movement_ A survey

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

[2005 TIP] Image change detection algorithms a systematic survey

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

[2007 CVIU] Vision-based human motion analysis An overview

[2007 IJCV] Pedestrian Detection via Periodic Motion Analysis

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

[2010 IVC] A survey on vision-based human action recognition

[2012 PAMI] Pedestrian Detection 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 the Shape of the Scene A Holistic Representation of the Spatial Envelope

[2001 PAMI] Visual Word Ambiguity

[2007 PAMI] A Thousand Words in a Scene

[2010 PAMI] Evaluating Color Descriptors for Object and Scene Recognition

[2011 PAMI] CENTRIST A Visual Descriptor for Scene Categorization

31.
Shadow Detection

[2003 PAMI] Detecting moving 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 MOMENT INVARIANTS FOR SHAPE DISCRIMINATION

[1993 PR] Pattern Recognition by Affine Moment Invariants

[1996 PR] IMAGE RETRIEVAL USING COLOR AND SHAPE

[2001 SMI] Shape matching similarity measures and algorithms

[2002 PAMI] Shape matching and object recognition using shape contexts

[2004 PR] Review of shape representation and description techniques

[2006 PAMI] Integral Invariants for Shape Matching

[2008] A Survey of Shape Feature 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] Object recognition from local scale-invariant features

[2000 IJCV] Evaluation of Interest Point Detectors

[2003 CVIU] Speeded-Up Robust Features (SURF)

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

[2004 IJCV] Distinctive Image Features from Scale-Invariant Keypoints

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

[2011 PAMI] SIFTflow Dense Correspondence 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] Simultaneous Localization and Map-Building Using Active Vision

[2007 PAMI] MonoSLAM Real-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 features for image classification

[1979 ] Statistical and structural approaches to texture

[1996 PAMI] Texture features for browsing and retrieval of image data

[2002 PR] Brief review of invariant texture analysis methods

[2012 TIP] Color Local Texture 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TLD
Vision 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 of robust object detectors during unstable tracking

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

[2010 ICIP] FACE-TLD TRACKING-LEARNING-DETECTION APPLIED TO FACES

[2012 PAMI] Tracking-Learning-Detection

37.
Video Surveillance

前两篇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 for Video Surveillance and Monitoring

[2000 PAMI] W4-- real-time surveillance of people and their activities

[2008 MVA] The evolution of 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 object detection using a boosted cascade of simple features

[2004 IJCV] Robust Real-time Face Detection

六、
结束语

历时一个多月，终于用业余时间把这些资料整理出来了，总算了却了一块心病，也不至于再看着一堆资料发愁了。以后可能会有些小修小补，但不会有太大的变化了。万里长征走完了第一步，剩下的就是理解和消化了。借新浪ishare共享出来，希望能够对你的科研也有一定的帮助。最后简单统计一下各个年份出现的频率。

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