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CS576 Computer Vision Instructor: Dr. Yu-Wing Tai No official TA Tuesday and Thursday 4:00pm 5:30pm Rm 3444 E3-1 Building 1 Slide 2 2 Who am I ? Course Webpage: http://yuwing.kaist.ac.kr/courses/CS576/index.html Instructor Info: Dr. Yu-Wing Tai yuwing@cs.kaist.ac.kryuwing@cs.kaist.ac.kr / yuwing@gmail.comyuwing@gmail.com Education: PhD National University of Singapore 2009 M.Phil Hong Kong University of Science and Technology 2005 B.Eng Hong Kong University of Science and Technology 2003 Research: Computer Vision, Image/Video Processing Web: http://yuwing.kaist.ac.kr/ Office hours: arrange by email. Slide 3 Grading (Absolute Grading) You start with the letter grade C-, you earn sub-grade from: 2 course projects (2 sub-grades) 1 term project (1-2 sub-grades) Peer review of term project (1 sub-grade) 1 oral examination (1 sub-grade) Bonus (See project description) Attendant (At least 60%, 1 sub-grade) Best Grade: A+ (No limit on number of A+) Worst Grade: D+ (No exception for better grade) 3 Slide 4 Project 1: Feature Detection and Matching Descriptions: http://yuwing.kaist.ac.kr/courses/CS576/project1/index.htmlhttp://yuwing.kaist.ac.kr/courses/CS576/project1/index.html Deadline: Friday mid-night (00:00) on Week 5 A project directly copy from the first project of the computer vision class in U. Washington 1 sub-grade for this project; 0.5 sub-grade for unsuccessful implementation; 0 sub-grade for late submission No bonus sub-grade for Project 1 Goal: Fundamental project for computer vision class Learning how to use skeleton codes from others Learning how to find related internet resources for project 4 Slide 5 Project 2: Paper implementation Descriptions: http://yuwing.kaist.ac.kr/courses/CS576/project2/index.htmlhttp://yuwing.kaist.ac.kr/courses/CS576/project2/index.html Deadline: Friday mid-night (00:00) on Week 9 1 sub-grade for this project; 0.5 sub-grade for unsuccessful implementation; 0 sub-grade for late submission Maximum 1 Bonus sub-grade for extra successful submission Goal: Paper reading Find your own interests area Learning how to find related resources Learning how to reproduce previous research projects 5 Slide 6 Term Project: A mini-conference submission Descriptions: http://yuwing.kaist.ac.kr/courses/CS576/termproject/index.htmlhttp://yuwing.kaist.ac.kr/courses/CS576/termproject/index.html Deadline: Friday mid-night (00:00) on Week 13 1 sub-grade for submission; 1 sub-grade for acceptance; 0 sub-grade for late submission Acceptance rate will be about 30% - 50% 1 sub-grade for peer evaluation Bonus sub-grade for best presentation Bonus sub-grade for 2 outstanding reviewers Bonus sub-grade(s) for extra-acceptance project Goal: Understand the process of research cycle and paper submission Testing your abilities/potentials in doing research/getting PhD 6 Slide 7 Oral Exam 10-15 minutes face-to-face question and answer session testing your knowledge and your understanding to the term project Week 15 1 sub-grade for passing; 0.5 sub-grade for failure; -1 sub-grade for absent Goal: Testing your knowledge Getting course feedback 7 Slide 8 Course Resources Books (Available for borrowing): Computer Vision: Algorithms and Applications 2010, Richard Szeliski Computer Vision: A Modern Approach 2002, David A. Forsyth and Jean Ponce Computer Vision papers: http://www.gmazars.info/conf/ http://www.gmazars.info/conf/ Computer Graphics papers: http://kesen.huang.googlepages.com/ http://kesen.huang.googlepages.com/ Microsoft Academic Search http://academic.research.microsoft.com/ http://academic.research.microsoft.com/ Google http://www.google.com/ http://www.google.com/ 8 Slide 9 Course Resources Computer Vision Source Codes http://www.cs.cmu.edu/~cil/v-source.html http://www.cs.cmu.edu/~cil/v-source.html OpenCV http://sourceforge.net/projects/opencvlibrary/ http://sourceforge.net/projects/opencvlibrary/ The Middlebury Computer Vision Pages http://vision.middlebury.edu/ http://vision.middlebury.edu/ Computer Vision Algorithm Implementations http://www.cvpapers.com/rr.html http://www.cvpapers.com/rr.html Computer Vision Datasets http://clickdamage.com/sourcecode/cv_datasets.html http://clickdamage.com/sourcecode/cv_datasets.html Columbia University Computer Vision Lab http://www.cs.columbia.edu/CAVE/ http://www.cs.columbia.edu/CAVE/ 9 Slide 10 Other Texts UNDERGRADUATE A Guided Tour of Computer Vision, by V. S. Nalwa, Addison- Wesley, 1993. Introductory Techniques for 3-D Computer Vision, by Emanuele Trucco, Alessandro Verri, Prentice-Hall, 1998 GRADUATE/ Specialized Reference Multiple View Geometry, by Richard Hartley, Andrew Zisserman, Cambridge University Press, 2000. Numerical Recipes in C, by William Press et al., Cambridge Univ Press, 1992. Pattern Classification and Scene Analysis, by Richard O. Duda, Peter E. Hart, John Wiley & Sons, 1973. Convex Optimization, by Stephen Boyd and Lieven Vandenberghe, 2010. 10 Slide 11 What is Computer Vision ? In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and getting the computer to describe what it saw In the 1960s, almost no one realized that machine vision was difficult. David Marr, 1982 40+ years later, we are still working on this 11 Slide 12 What is Computer Vision ? 12 Slide 13 The goal of computer vision To bridge the gap between pixels and meaning 13 Slide 14 What is Computer Vision ? 14 From Wikipedia Slide 15 What is Computer Vision ? 15 Computer Vision Image Processing Computer Graphics Stereo, 3D Reconstruction, etc. Image Enhancement, Denoising, etc. Rendering, 3D Modelling, etc. Slide 16 1970s 16 line labeling pictorial structures articulated body model intrinsic imagesstereo correspondenceoptical flow Slide 17 1980s 17 pyramid blendingshape from shadingedge detection physically-based models regularization-based surface reconstruction range data acquisition and merging Slide 18 1990s 18 factorization-based structure from motion dense stereo matching multi-view reconstruction face trackingimage segmentation face recognition Slide 19 2000s 19 image-based rendering image-based modeling Interactive Techniques texture synthesis feature-based recognitionregion-based recognition Slide 20 State-of-the-art ? 20 http://www.xbox.com/en-US/kinect Slide 21 Why study computer vision ? 21 Slide 22 Why study computer vision ? Vision is useful Vision is interesting Vision is difficult Half of primate cerebral cortex is devoted to visual processing Achieving human level visual perception is probably AI complete 22 Slide 23 Challenges: viewpoint variation 23 Slide 24 Challenges: Illumination 24 Slide 25 Challenges: Scale 25 Slide 26 Challenges: Deformation 26 Slide 27 Challenges: Occlusion 27 Slide 28 Challenges: Background cluster 28 Slide 29 Challenges: Motion 29 Slide 30 Challenges: Object intra-class variation 30 Slide 31 Challenges: Local Ambiguities 31 Slide 32 Challenges or opportunities? Images are confusing, but they also reveal the structure of the world through numerous cues Our job is to interpret the cues! 32 Slide 33 Computer Vision in the Real World Special Effects in movie 33 Slide 34 Computer Vision in the Real World 3D Urban Modeling 34 Slide 35 Computer Vision in the Real World Microsoft Photosynth (http://labs.live.com/photosynth/)http://labs.live.com/photosynth/ 35 Slide 36 Computer Vision in the Real World Face Detection 36 Slide 37 Computer Vision in the Real World Biometrics 37 Slide 38 Computer Vision in the Real World Optical Character Recognition (OCR) 38 Slide 39 Computer Vision in the Real World Toys and Robots 39 Slide 40 Computer Vision in the Real World Games 40 Slide 41 Computer Vision in the Real World Automotive Safety 41 Slide 42 Computer Vision in the Real World Vision for robotics, space exploration 42 Slide 43 Goal of this course Broaden your view about computer vision, but we are not going to study any specific topic in deep Teaching you how to find computer vision research resources yourself Understand the research world and teaching you how to be a good computer vision scientist 43 Slide 44 Question ? 44