计算机视觉-1

《计算机视觉》学习计划 2013年3月至7月 第4周~20周，每周四上午8:00~11:30 学习方式：基于指定内容，结合有关论文，自学、轮讲、讨论、算法实现。 主要教材： · 英文版：Computer Vision: Algorithms and Applications. Richard Szeliski 电子版及有关资料：http://szeliski.org/Book/ · 中译本：计算机视觉：算法与应用。艾海舟等。 其他参考教材： · Forsyth, David A., and Ponce, J. Computer Vision: A Modern Approach(second edition), Prentice Hall, 2012. · Hartley, R. and Zisserman, A. Multiple View Geometry in Computer Vision, Academic Press, 2002. 以上两本书中：第一本的第一版有中译本、第二版英文版已在大陆出版； 第二本的中译本为“计算机视觉中的多视图几何”，安徽大学出版社。 两本书的中译本，感兴趣的可在淘宝网上买。 本学期课程内容主要基于指定教材的前6章、第14章 周次(日期) 主 主要任务 4(3.21) 绪论 图像形成 教材：第1章概述：什么是计算机视觉，哪些不是计算机视觉范畴的？计算机视觉在各领域的典型应用；计算机视觉经历了哪些主要发展阶段；计算机视觉面临的挑战有哪些(上、概念上) 阅读：详细阅读第1章; 阅读 http://cirl.lcsr.jhu.edu/Introduction Image Processing in Matlab: pdf 安装: OpenCV, MATLAB及相关工具箱 能实现图像数据的读取、显示、转存。 --------------------------------------------------------------- 第2章图像形成：主要了解图像形成的相关背景。 阅读内容：详细阅读第2.2~2.5； http://cirl.lcsr.jhu.edu/Background ； Color: pdf  http://cs.nyu.edu/~fergus/teaching/vision/4_color.pdf Lighting, Color (Slides - ) (Slides - PDF) A description of how CCD cameras work A comparison with CMOS sensors NVDIA-BRDF Tutorial 摄像头成像基础(成像原理；成像传感器；投影模型；光照模型；数字图像的生成；图像空间分辨率、颜色深度)；图像的表示(几种典型图像文件格式BMP,GIF,PNG,JPEG,TIFF,PGM,PPM, RAW,，颜色空间模型以及不同颜色模型的转换) CS 143 Introduction to Computer Vision http://cs.brown.edu/courses/cs143/ Fall 2011, MWF 11:00 to 11:50, CIT 368. Instructor: James Hays TAs: Evan Wallace (HTA), Sam Birch, Paul Sastrasinh, Libin "Geoffrey" Sun, and Vazheh Moussavi. Course Description Course Catalog Entry How can computers understand the visual world of humans? This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic, statistical, data-driven approaches. Topics include image processing; segmentation, grouping, and boundary detection; recognition and detection; motion estimation and structure from motion. This offering of CS 143 will emphasize the core vision task of recognition in particular. We will train and evaluate classifiers to recognize various visual phenomena. The course will consist of five programming projects, two written quizzes, and a self-chosen final project. Students can earn graduate credit for the course but will need to meet higher requirements on all projects throughout the semester and need the instructor's permission. This course can satisfy the graduate A.I area requirement. Prerequisites This course requires programming experience as well as linear algebra, basic calculus, and basic probability. Previous knowledge of visual computing will be helpful. The following courses (or equivalent courses at other institutions) are helpful prerequisites: · CS 123, Introduction to Computer Graphics · CS 129, Computational Photography · CS 195-F, Introduction to Machine Learning Some of the course topics overlap with these related courses, but none of the assignments will. Assignments Winning projects All Results Hybrid images with Laplacian pyramids Andy Loomis, Emanuel Zgraggen, Dylan Field Project 1 results pB Lite: boundary detection Paul Sastrasinh, Li Sun, Hang Su Project 2 results Scene recognition with bag of words Paul Sastrasinh, Chen Xu, Yun Zhang Project 3 results Face detection with a sliding window Emanuel Zgraggen, Hang Su, Paul Sastrasinh Project 4 results Tracking and Structure from Motion or ... Hang Su Project 5 results Your choice for final project Seth Goldenberg, Emanuel Zgraggen Final project results It is strongly recommended that all projects be completed in Matlab. All starter code will be provided for Matlab. Students may implement projects through other means but it will generally be more difficult. Textbook Readings will be assigned in "Computer Vision: Algorithms and Applications" by Richard Szeliski. The book is available for free online or available for purchase. Grading Your final grade will be made up from · 80% 5 programming projects · 20% 2 written quizzes You have three "late days" for the whole course. That is to say, the first 24 hours after the due date and time counts as 1 day, up to 48 hours is two and 72 for the third late day. This will not be reflected in the initial grade reports for your assignment, but they will be factored in and distributed at the end of the semester so that you get the most points possible. Graduate credit is available and each project will specifiy the minimum requirements to earn such credit. Important Links: · Collaboration Policy · Matlab Tutorial Contact Info and Office Hours: You can contact the professor or TA staff with any of the following: · James: hays[at]cs.brown.edu · HTA and Professor: cs143headtas[at]cs.brown.edu · TAs and Professor: cs143tas[at]cs.brown.edu James' office hours will be held in his office (CIT 445). TA office hours will be held in the Brindy Bowl (CIT 271). · James Hays (hays), Monday and Wednesday 1:00-2:00 · Libin "Geoffrey" Sun (lbsun), Monday 7-9pm · Paul Sastrasinh (psastras), Tuesday 7-9pm · Sam Birch (sbirch), Wednesday 7-9pm · Evan Wallace (edwallac), Thursday 7-9pm · Vazheh Moussavi (vmoussav), Friday 5-7pm Tentative Syllabus Class Date Topic Slides Reading Projects W, Sept 7th Introduction to computer vision .ppt, .pdf Szeliski 1 Image Formation and Filtering F, Sep 9th Cameras and optics .ppt, .pdf Szeliski 2.1, especially 2.1.5 Project 1 out M, Sep 12th Light and color .ppt, .pdf Szeliski 2.2 and 2.3 W, Sep 14th Pixels and image filters .ppt, .pdf Szeliski 3.2 F, Sep 16th Thinking in frequency .ppt, .pdf Szeliski 3.4 M, Sep 19th Image pyramids and applications .ppt, .pdf Szeliski 3.5.2 and 8.1.1 Machine Learning Crash Course W, Sep 21st Machine learning: overview .ppt, .pdf F, Sep 23rd Machine learning: clustering .ppt, .pdf Szeliski 5.3 M, Sep 26th Machine learning: classification .ppt, .pdf Project 1 due Grouping and Fitting W, Sep 28th Edge detection and line fitting w/ Hough transform .ppt, .pdf Szeliski 4.2 Project 2 out F, Sep 30th Robust fitting (Hough Transform) .ppt, .pdf Szeliski 4.3 M, Oct 3rd Robust fitting (RANSAC and others) .ppt, .pdf Szeliski 4.3 W, Oct 5th Mixture of Gaussians and EM .ppt, .pdf F, Oct 7th Gestalt cues, MRFs, and graph cuts .ppt, .pdf Szeliski 5.5 M, Oct 10th No classes Project 2 due Recognition W, Oct 12th Recoginition Overview and History .ppt, .pdf Szeliski 14 Project 3 out F, Oct 14th Image features and bag of words models .ppt, .pdf Szeliski 4.1.2, 14.4.1, and 14.3.2 M, Oct 17th Interest points: corners .ppt, .pdf Szeliski 4.1.1 W, Oct 19th Quiz 1 F, Oct 21st Interest points and instance recognition .ppt, .pdf Szeliski 14.3 M, Oct 24th Large-scale instance recognition .ppt, .pdf Szeliski 14.3.2 Project 3 due W, Oct 26th Detection with sliding windows .ppt, .pdf Szeliski 14.1 F, Oct 28th Guest talk: Jim Rehg, Behavior Imaging and the Study of Autism M, Oct 31st Detection with sliding windows continued .ppt, .pdf Szeliski 14.2 Project 4 out W, Nov 2nd Context and Spatial Layout .ppt, .pdf Szeliski 14.5 F, Nov 4th Guest talk: Gabriel Taubin, 3d photography Multiple Views and Motion M, Nov 7th Feature Tracking .ppt, .pdf Szeliski 4.1.4 W, Nov 9th Optical Flow see above Szeliski 8.4 F, Nov 11th Guest lecture: Deqing Sun, Optical flow Project 4 due M, Nov 14th Epipolar Geometry .ppt, .pdf Szeliski 11 W, Nov 16th Stereo Correspondence .ppt, .pdf Project 5 out F, Nov 18th Structure from Motion .ppt, .pdf Szeliski 7 Final Project out Advanced Topics M, Nov 21st Activity Recognition .ppt, .pdf W, Nov 23rd No classes F, Nov 25th No classes M, Nov 28th Internet Scale Vision .ppt, .pdf W, Nov 30th Guest lecture: Pedro Felzenszwalb, Object Detection .pdf F, Dec 2nd Crowdsourcing .ppt, .pdf M, Dec 5th Attributes and Course Summary .ppt, .pdf W, Dec 7th Quiz 2 F, Dec 9th No classes, reading period M, Dec 12th No classes, reading period Final Project / Project 5 due T, Dec 13th, 9:00 AM Exam Period - final presentations 关闭提示 关闭 确 认 取 消