Digital Holography

(Computational Photography)

About Instructor

Seungkyu Lee, Assistant Prof., Dept. of CE

Ph.D at Penn State Univ. US, MS & BS at KAIST

Researcher, KBS TRI - 6 Years

Senior Researcher, Samsung (SAIT) – 3.5 Years

Research Field

: Computer Vision

- 3D Reconstruction & Interaction using depth cameras

- Object/Gesture/Pattern Recognition

- Computational Photography

Office: Room 310-2 (Find my name!!!)

Email: seungkyu@khu.ac.kr

Lab: Room 436

Members: 1 Professor, 6 Grad. Stud.

Webpage: http://cvlab.khu.ac.kr/

What is Computer vision?

(Some slides are from Prof. James Hays, Brown Univ.)

Computer Vision

Make computers understand images and video.

What kind of scene?

Where are the cars?

How far is the

building?

…

Vision is really hard

• Vision is an amazing feat of natural intelligence – Visual cortex occupies about 50% of Macaque brain

– More human brain devoted to vision than anything else

Is that a queen or a bishop?

Why computer vision matters

Safety Health Security

Comfort Access Fun

Ridiculously brief history of computer vision

• 1966: Minsky assigns computer vision as an undergrad summer project

• 1960’s: interpretation of synthetic worlds

• 1970’s: some progress on interpreting selected images

• 1980’s: ANNs come and go; shift toward geometry and increased mathematical rigor

• 1990’s: face recognition; statistical analysis in vogue

• 2000’s: broader recognition; large annotated datasets available; video processing starts

• 2030’s: robot uprising?

Guzman ‘68

Ohta Kanade ‘78

Turk and Pentland ‘91

Optical character recognition

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

Face detection

• Many new digital cameras now detect faces

Smile detection

Sony Cyber-shot® T70 Digital Still Camera

3D from thousands of images

Building Rome in a Day: Agarwal et al. 2009

Object recognition

LaneHawk by EvolutionRobotics

“A smart camera is flush-mounted in the checkout lane,

continuously watching for items. When an item is detected and

recognized, the cashier verifies the quantity of items that were

found under the basket, and continues to close the transaction. The

item can remain under the basket, and with LaneHawk,you are

assured to get paid for it… “

Vision-based biometrics

“How the Afghan Girl was Identified by Her Iris Patterns” Read the

story

wikipedia

Login without a password…

Fingerprint scanners on

many new laptops,

other devices

Face recognition systems now

beginning to appear more widely http://www.sensiblevision.com/

Object recognition

Point & Find, Nokia

Google Goggles

The Matrix movies, ESC Entertainment, XYZRGB, NRC

Special effects:shape capture

Pirates of the Carribean, Industrial Light and Magic

Special effects:motion capture

Smart cars

• Mobileye

– Vision systems currently in high-end BMW, GM, Volvo models

– By 2010: 70% of car manufacturers.

Slide content courtesy of Amnon Shashua

Google cars

Oct 9, 2010. "Google Cars Drive Themselves, in Traffic". The New York Times.

John Markoff

June 24, 2011. "Nevada state law paves the way for driverless cars". Financial

Post. Christine Dobby

Aug 9, 2011, "Human error blamed after Google's driverless car sparks five-

vehicle crash". The Star (Toronto)

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

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.

Industrial robots

Vision-guided robots position nut runners on wheels

Mobile robots

http://www.robocup.org/

NASA’s Mars Spirit Rover

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

Saxena et al. 2008

STAIR at Stanford

Medical imaging

Image guided surgery

Grimson et al., MIT 3D imaging

MRI, CT

My Research Topics

3D by Color + Depth Fusion

ToF or Kinect Depth Cameras

Depth Image

3D Depth Points

RGB+D Research

3D Modeling 3D Motion

3D Control 3D Recognition

3D Modeling Applications

• 3D Animation

• 3D Printing

3D Animation • Physical to Virtual

3D Printing • Physical to Virtual to Physical

Future 3D Displays

USC, Volumetric Display SeeReal, Hologram Display Holografika

LF Model of Real Scene

Pattern Recognition

Symmetry pattern detection

Rotation symmetry

Reflection symmetry

Curved glide-reflection

symmetry

Translation symmetry

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Experimental Results

31 Zebra fish

Lizard

Leaves

Spine

Caterpillar

Rotation/Reflection Symmetry Detection

• S. Lee, Y. Liu, "Curved Glide Reflection Symmetry Detection", IEEE TPAMI 2012 • S. Lee, Y. Liu, "Skewed Rotation Symmetry Group Detection", IEEE TPAMI 2010 • S. Lee, Y. Liu, "Curved Glide Reflection Symmetry Detection", IEEE CVPR 2009 (Oral) • S. Lee, R.Collins,Y.Liu,"Rot. Sym. Group Detection Via Freq. Analysis of Frieze-Expansions“, IEEE CVPR 2008 • M. Park & S. Lee et al. "Performance Eval. of State-of-the-Art Disc. Sym. Detection Alg.s“, IEEE CVPR 2008

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Symmetry in our culture

- Spatiotemporal symmetries from human body motion / cultural activity

- Symmetry in 3D geometry (Niloy Mitra)

Symmetry Gesture/Shape/Object Detection

• S. Lee, “Frequency Guided Bilateral Symmetry Gabor Wavelet Network”, IEEE ICIP 2011 • S. Lee, Y. Liu, "Symmetry-Driven Shape Matching", PSU-CSE-09011, CSE dept. Penn State University 2009 • M. Park, S. Lee, "Face Modeling and Tracking with Gabor Wavelet Network Prior", ICPR 2008 • S. Lee, Y. Liu, R.Collins,"Shape variation-based Frieze Pattern for Robust gait recognition", IEEE CVPR 2007

Motion texture

Activity Recognition (Data: Biomotion)

Confusion matrix

Recognition results (108 subjects)

- 10 activity types

- 50 training, 50 test sets

- Forward selection + nAVR

- Classifier = LDA

100%

0%

In this class?

First Half

Introduction of Computer Vision Topics

Two take home exams

: Two implementation problems for each (Matlab, C++, JAVA, …)

: Discussion is allowed (actually encouraged!!)

: But implement by yourself and submit (working code package)

individually

: No existing library

: Have to include as many annotation lines as possible

(evaluation will be based on your annotations)

(for example, working code without any annotation will get 0 grade)

: Copied submission (from internet or from other students)

will get MINUS grade

Reference Books: "Computer Vision: Algorithms and Applications" by Richard Szeliski

Second Half

Introduction of Computer Vision Topics

Final Project (Competition !!)

: Choose one topic from given list (3 or 4 selected topics)

: Submit project proposal

: Implement your own algorithm and test on given dataset

: Implement by yourself as many parts as possible

: Submit working code package and final report

: Refer if you have adopted any existing algorithm

: Each your algorithm will be evaluated qualitative and quantitatively

: Tips for high grade?

Win the competition~

Propose a new idea and algorithm (extra grade)

Analyze every your algorithm and results in your report