Lecture 1 Introduction to Computer Vision Lin ZHANG, PhD School of Software Engineering, Tongji University Spring 2014
Course Info Contact Information Room 314, Jishi Building Email: cslinzhang@tongji.edu.cn Tel: 69585491 TA: Lida LI, lld533@hotmail.com QQ: 260459856 Course information can be found at http://sse.tongji.edu.cn/linzhang
Materials Major materials My slides References Some papers Milan Sonka, Vaclav Hlavac, and Roger Boyle, Image Processing, Analysis, and Machine Vision, Thomson, 2008 D.A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Pearson Education, Inc., 2003
Examination Homework 45%: 3 times, and each time 15%. Project 50%: 2 or 3 people for one group Attendance 5% (being absent >=5 times, you will fail this course) Bonus 5%: being active in class and answering my questions correctly
Today What is computer vision? Course overview Course requirement
What is computer vision? To bridge the gap between pixels and meaning Source: S. Narasimhan What we see What a computer sees
What is computer vision? Source: Feifei Li
Human vision sclera choroid blind spot
Human vision
What is it related to? Source: Feifei Li
Vision as a measurement device Real time stereo Structure from motion Reconstruction from Internet photo collections NASA Mars Rover Pollefeys et al. Goesele et al.
Vision as a source of semantic information slide credit: Fei Fei, Fergus & Torralba
Object categorization sky building flag banner bus face street lamp bus wall cars slide credit: Fei Fei, Fergus & Torralba
Scene and context categorization outdoor city traffic slide credit: Fei Fei, Fergus & Torralba
Why study computer vision? Source: Lazebnik
Why study computer vision? Vision is useful: Images and video are everywhere! Personal photo albums Movies, news, sports Surveillance and security Medical and scientific images
Why computer vision is difficult? Challenges: viewpoint variation Michelangelo 1475 1564
Why computer vision is difficult? Challenges: illumination
Why computer vision is difficult? Challenges: scale slide credit: Fei Fei, Fergus & Torralba
Why computer vision is difficult? Challenges: deformation Xu, Beihong 1943 Source: Feifei Li
Why computer vision is difficult? Challenges: occlusion Magritte, 1957
Why computer vision is difficult? Challenges: background clutter
Why computer vision is difficult? Challenges: Motion
Why computer vision is difficult? Challenges: object intra class variation Source: Feifei Li
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! Image source: J. Koenderink
Depth cues: Linear perspective
Depth cues: Aerial perspective
Depth ordering cues: Occlusion
Shape cues: Texture gradient
Grouping cues: Similarity (color, texture, proximity)
Typical CV applications
Earth Viewers (3D modeling) Image from Baidu 3D Map
Photosynth Project products of students from 2009 Media&Arts
Structure from motion Bundler: Structure from Motion (SfM) for Unordered Image Collections (https://www.cs.cornell.edu/~snavely/bundler/#s3)
Optical character recognition (OCR) Technology to convert scanned docs to text If you have a scanner, it probably came with OCR software Digit recognition, AT&T labs http://www.research.att.com/~yann/ License plate readers http://en.wikipedia.org/wiki/automatic_number_plate_recognition Source: S. Seitz
Face detection Many new digital cameras now detect faces Canon, Sony, Fuji, Source: S. Seitz
Smile detection Source: S. Seitz
Vision-based biometrics How the Afghan Girl was Identified by Her Iris Patterns
Login without a password Palmprint system Fingerprint scanners on many new laptops, other devices Finger Knuckle Print system
Face verification National Stadium, Beijing Olympic Games, 2008
Object recognition (in mobile phones) Source: S. Seitz
Special effects: motion capture Source: S. Seitz Pirates of the Carribean, Industrial Light and Magic
Vision in space NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007. Vision systems (JPL) used for several tasks Panorama stitching 3D terrain modeling Obstacle detection, position tracking For more, read Computer Vision on Mars by Matthies et al.
Robotics NASA s Mars Spirit Rover http://en.wikipedia.org/wiki/spirit_rover http://www.robocup.org/
Robotics Itti s system
Medical imaging 3D imaging MRI, CT Image guided surgery Grimson et al., MIT
Course content (just a plan) Introduction Image filtering Local interest point detectors Local feature descriptors and matching Camera models Biometrics: Theories and applications Face detection and face recognition Texture Object recognition: BoW model 3D shape recognition* Background subtraction*
Some tips Prerequisites Linear algebra Calculus Matlab Programming C++ Programming Knowledge sources IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) IEEE Transactions on Image Processing (TIP) International Journal of Computer Vision (IJCV) IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) IEEE International Conference on Computer Vision (ICCV) European Conference on Computer Vision (ECCV)
Thanks for your attention