CS576: Computer Vision - KAISTvclab.kaist.ac.kr/cs576/slide01-intro.pdf · Lecturer: Min H. Kim (KAIST) CS576: Computer Vision About Instructor • Min Hyuk Kim (김민혁) – Associate

Lecturer: Min H. Kim (KAIST) CS576: Computer Vision

CS576: Computer Vision

Min H. KimKAIST School of Computing

General course introduction


Welcome

• [CS576] Computer Vision

• Professor: Min H. Kim (김민혁)([email protected])

• (Lecture) Monday and Wednesday 1:00PM—2:30PM, Rm. 117, Bldg. N-1

• Course website: http://vclab.kaist.ac.kr/cs576/

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About Instructor• Min Hyuk Kim (김민혁)– Associate Professor at KAIST School of Computing since 2012

– Leading the Visual Computing Lab. (KAIST VCLAB) in School of Computing

– PhD in Computer Science (University College London, UCL 2010)

– Postdoc Researcher at Yale University

• Research Areas: Computer Graphics and Vision– Computational Imaging & Computational Photography

– Image-Based Modeling and Rendering

– Color & Visual Perception


About Instructor• Min Hyuk Kim (김민혁)– ACM SIGGRAPH Asia Technical Paper Committee (TPC) 2017, 2018– Eurographics Technical Paper Committee (TPC) 2018, 2019– Associate Editor of ACM Trans. Graphics (TOG) 2014--2017– Associate Editor of ACM Trans. Applied Perception (TAP) 2016--

Present– KAIST Top 10 Research Accomplishments 2016– Microsoft New Faculty Award 2013– Naver Young Faculty Fellowship 2015

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Textbook• Reference book: Richard

Szeliski (2010) Computer Vision: Algorithms and Applications, Springer.

• PDF version is available from SpringerLink within the KAIST network.

• https://link.springer.com/book/10.1007%2F978-1-84882-935-0

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https://link.springer.com/book/10.1007/978-1-84882-935-0


Timeline

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Low-level computer

vision(geometry, 3D

imaging)

High-level computer

vision (detection,

recognition)

Computational photography

(HDR and light-field imaging)

Machine-learning vision (CNN, GAN, RL,

etc.)


Notification• There are no official course prerequisites.

• [Warning] However, we assume in CS576 – We do not teach general mathematics such as

linear algebra and calculus.

– Homework assignment will require programming experience in MATLAB and Python!

– We do not provide basic programming skills.

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Grading

• Class participation: 10%

• Midterm/final exam: 50% (25% each)

• Programming assignments (about 4 HWs): 30%

• Quizzes for every lecture: 10%

• An assignment after its original due date will be degraded from the marked credit per day:e.g., A à B for one-day delayed submission.

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What we will do in this course• Checking your attendances for every lecture

• Quiz at the beginning of every lecture!

• Homework plagiarism– First, no grade for copied codes (such as your friends or

codes on Internet) is given! – Second, you will get F accordingly!

Your cheating will be reported to the school.– Do not cheat by copying others’ codes!– Our TA are using an advanced code plagiarism detection

software!• Midterm and final term examinations (all about math in

computer vision, not open-book, no memory test!)

• Let’s have fun! J9


Teaching Assistants• Sukjun Jeon (VCLAB)– [email protected]

• Hyunho Ha (VCLAB)– [email protected]

• Hyeonjoong Jang (VCLAB)– [email protected]

• Shinyoung Yi (VCLAB)– [email protected]

• Inseung Hwang (VCLAB)– [email protected]

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mailto:[email protected]






INTRODUCTION TO COMPUTER VISION

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Acknowledgement: many slides here are borrowed from Professor James Tompkin at Brown University, USA, and Reinhard Klette at Auckland University of Technology, New Zealand.


What is computer vision?Have you ever used computer vision?How? Where?


What is computer vision?Jitendra Malik, UC BerkeleyThree ‘R’s of Computer Vision


What is computer vision?Jitendra Malik, UC BerkeleyThree ‘R’s of Computer Vision

“The classic problems of computational vision:reconstruction (in 3D)recognition(re)organization.”


What is computer vision?Have you ever used computer vision?How? Where?Reconstruction? Recognition? (Re)organization?


Optical character recognition (OCR)

Digit recognition, AT&T labs

http://www.research.att.com/~yann/

Technology to convert scanned docs to text

• If you have a scanner, it probably came with OCR software

License plate readers

http://en.wikipedia.org/wiki/Automatic_number_plate_recognition

JH

http://www.research.att.com/~yann

http://en.wikipedia.org/wiki/Automatic_number_plate_recognition


Face detection

• Almost all digital cameras detect faces• Snapchat face filters





Smile detection

Sony Cyber-shot® T70 Digital Still Camera JH

http://www.sonystyle.com/webapp/wcs/stores/servlet/ProductDisplay?catalogId=10551&storeId=10151&productId=8198552921665200469&langId=-1


Object recognition (in supermarkets)


How does it work?


Vision-based biometrics

“How the Afghan Girl was Identified by Her Iris Patterns” Read the story (Wikipedia)

JH

http://www.cl.cam.ac.uk/~jgd1000/afghan.html

http://en.wikipedia.org/wiki/Afghan_Girl_(photo)


Login without a password…





Liang et al. 2014


Object recognition (in mobile phones)

e.g., Google Lens


3D from images

Building Rome in a Day: Agarwal et al. 2009


Human shape capture


Human shape capture


Human shape capture


Human shape capture

Lecturer: Min H. Kim (KAIST) CS576: Computer VisionStar Wars: Rogue One – Peter Cushing / Admiral Tarkin

Special effects: shape capture

http://cinetropolis.net/tarkin-care-of-business-rogue-ones-digital-peter-cushing/


Special effects: shape capture


Special effects: motion capture


Interactive Games: Kinect• Object Recognition:

http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o• Mario: http://www.youtube.com/watch?v=8CTJL5lUjHg• 3D: http://www.youtube.com/watch?v=7QrnwoO1-8A• Robot: http://www.youtube.com/watch?v=w8BmgtMKFbY

JH

http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o

http://www.youtube.com/watch?v=8CTJL5lUjHg

http://www.youtube.com/watch?v=7QrnwoO1-8A

http://www.youtube.com/watch?v=w8BmgtMKFbY


Sports

Sportvision first down lineNice explanation on www.howstuffworks.com

http://www.sportvision.com/video.html

JH

http://www.howstuffworks.com/first-down-line.htm

http://www.howstuffworks.com/

http://www.sportvision.com/video.html


Medical imaging

Image guided surgeryGrimson et al., MIT3D imaging

MRI, CT

JH

http://groups.csail.mit.edu/vision/medical-vision/surgery/surgical_navigation.html


AutoCars - Uber bought CMU’s lab




Industrial robots

Vision-guided robots position nut runners on wheels

JH


Vision in space

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.

NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.

JH

http://www.ri.cmu.edu/pubs/pub_5719.html

http://marsrovers.jpl.nasa.gov/gallery/images.html


Mobile robots

http://www.robocup.org/NASA’s Mars Spirit Rover

http://en.wikipedia.org/wiki/Spirit_rover

Saxena et al. 2008STAIR at Stanford

JH

http://www.robocup.org/

http://en.wikipedia.org/wiki/Spirit_rover

http://stair.stanford.edu/


Augmented Reality and Virtual Reality

MS HoloLens, Oculus, Magic Leap,ARCore / ARKit


What is computer vision?

Jitendra Malik, UC BerkeleyThree ‘R’s of Computer Vision

“[Further progress in] the classic problems of computational vision:

reconstructionrecognition(re)organization

[requires us to study the interaction among these processes].”


History• Origins in Early 1960s

• First Textbook– Azriel Rosenfeld. Picture Processing by Computer.

Academic Press, New York, 1969.

• First Journal– Computer Graphics and Image Processing (1972

– 1982) founded by Azriel Rosenfeld, – after 1982 continued as Computer Vision, Graphics,

and Image Processing48


Conference history• Int. Conf. Pattern Recognition (ICPR)– Washington D.C. 1973, chair: King-Sun Fu

• IEEE Conf. Computer Vision Pattern Recognition (CVPR)– Arlington 1983, chairs: Takeo Kanade and Dana Ballard

• Int. Conf. Computer Vision (ICCV)– London 1987, chairs: J. M. Brady and Azriel Rosenfeld

• European Conf. Computer Vision (ECCV)– Antibes 1990, chair: Olivier Faugeras

• Asian Conf. Computer Vision (ACCV)– Osaka 1993, chair: K. Sugihara

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State of the art today?With enough training data, computer vision nearly matches human vision at most recognition tasks

Deep learning has been an enormous disruption to the field. Many techniques are being “deepified”.

JH


Computer Vision and Nearby FieldsDerogatory summary of computer vision:

“Machine learning applied to visual data.”

JH


Computer Vision and Nearby Fields

Derogatory summary of computer vision:“Machine learning applied to visual data.”

JH

Model of the world

Images, videos,sensor data…

Images, videos,interaction

Digital worldReal world

Computer Graphics Computer Vision

Question answering

Documents

CS576: Computer Vision - KAISTvclab.kaist.ac.kr/cs576/slide01-intro.pdf · Lecturer: Min H. Kim (KAIST) CS576: Computer Vision About Instructor • Min Hyuk Kim (김민혁) – Associate