[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
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