EECS 274 Computer Vision

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14 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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EECS 274 Computer Vision

Introduction

What is computer vision?


Terminator 2

Every picture tells a story


Goal of computer vision is to write computer
programs that can interpret images

Can computers match (or beat) human vision?


Yes and no (but mostly no!)


humans are much better at “hard” things


computers can be better at “easy” things

Optical illusions

Copyright
A.Kitaoka

2003

Why is computer vision difficult?


Inverse problem


Ill
-
posed


High
-
dimensional data


Noise


Variation

Earth viewers (3D modeling)

Image from Microsoft’s
Virtual Earth

(see also:
Google Earth
)

Google streetview

Photosynth

http://labs.live.com/photosynth/

http://www.youtube.com/watch?v=p16frKJLVi0

by Noah Snavely, Steve Seitz, and Rick Szeliski

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


Canon, Sony, Fuji, …


Smile detection

Sony Cyber
-
shot® T70 Digital Still Camera

Object recognition
(in supermarkets)

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… “

Face recognition

Who is she?

Vision
-
based biometrics


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

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 (in mobile
phones)


This is becoming real:



Microsoft Research


Point & Find
,
Nokia
,
NTT Docomo

The Matrix

movies, ESC Entertainment, XYZRGB, NRC

Special effects: shape capture

Bullet time:

http://www.youtube.com/watch?v=J5ryLMZTO5M

Pirates of the Carribean
, Industrial Light and Magic

Click here for interactive demo

Special effects: motion capture

Sports

Sportvision

first down line

Nice
explanation

on www.howstuffworks.com

http://www.youtube.com/watch?v=UyPU2l9rdvo

Smart cars


Mobileye


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


By 2010: 70% of car manufacturers.


Video demo

Vision
-
based interaction (and
games)

Nintendo Wii has camera
-
based IR

tracking built in. See
Lee’s work at

CMU
on clever tricks on using it to

create a
multi
-
touch display
!

Digimask
: put your face on a 3D avatar.


Game turns moviegoers into Human Joysticks”
,
CNET

Camera tracking a crowd, based on
this work
.

Vision
-
based HCI


Reatrix:
http://www.youtube.com/watch?v=QzsQKULMbiU


Gaming


Sony Eyetoy


Microsoft Natal

http://www.youtube.com/watch?v=AOXoh
r4XE
-
4&feature=related

http://www.youtube.com/watch?v=1BRSf
CuLYHc

Motion capture


Marker
-
based motion capture


http://www.youtube.com/watch?v=V0yT8mwg9nc


Organic motion


http://www.organicmotion.com/

Looking at people


Hand gesture


Head pose


Expression


Identity

http://www.youtube.com/watch?v=NwVBzx0LMNQ

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.

Gigapan


http://www.gigapan.org/index.php


HP TouchSmart with Gigapn demo at
Chicago O’Hare airport


Robotics

http://www.robocup.org/


NASA’s Mars Spirit Rover

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

Medical imaging

Image guided surgery

Grimson et al., MIT

3D imaging

MRI, CT

Digital comestics


Inpainting

Bertalmio et al. SIGGRAPH 00

Debluring

Fergus et al. SIGGRAPH 06

Digital photo albums


Picasa, Flickr, Photobucket, etc.


Categorization


Tagging


Search


Computational photography


Image acquisition


Hardware/software


Optics


Shuttle speed


Novel sensors


Multiple camera


Multiple shots


Multi flash


Applications: high dynamic range imaging, super
resolution, photomontage, panorama moasicing,
debluring, light field, camera projector system…

Image and video search


Google


YouTubes


Microsoft


Yahoo

Current state of the art


You just saw examples of current systems.


Many of these are less than 5 years old


This is a very active research area, and rapidly
changing


Many new applications in the next 5 years


To learn more about vision applications and companies


David Lowe
maintains an excellent overview
of vision companies


http://www.cs.ubc.ca/spider/lowe/vision.html


Confluence of vision, graphics, learning,
sensing and signal processing

Software and hardware



Algorithms: processing images and videos


Camera: acquiring images/videos


Embedded system

Topics


Image formation: camera model, camera calibration,
radiometry, color, shading


Early vision: stereopsis, structure from motion,
illumination, reflectance, shape from X, texture


Mid
-
level vision: segmentation, grouping, Kalman filter,
particle filter, shape representation


High
-
level vision: correspondence, matching, object
detection, object recognition, visual tracking


Recent topics: image and video retrieval, internet vision

Related topics


Textbooks and references


Textbook


Computer Vision: A Modern Approach
, David Forsyth and Jean Ponce


Computer Vision: Algorithms and Applications

(draft), Richard Szeliski


Reference for background study:




Introductory Techniques for 3
-
D Computer Vision, Emanuele Trucco and
Alessandro Verri


Multiple View Geometry in Computer Vision, Richard Hartley and Andrew
Zisserman


An Invitation to 3
-
D Vision by Yi Ma, Stefano Soatto, and Jana Kosecka


Robot Vision, Berthold Horn


Learning OpenCV: Computer Vision with OpenCV Library, Gary Bradski and
Adrian Kaehler


Reading assignments will be from the text and additional material
that will be handed out or made available on the web page


All lecture slides will be available on the course website


http://faculty.ucmerced.edu/mhyang/course/cse274/index.htm

Grading


Based on projects


No midterm or final


20% Homework


40% Programming assignments


40% Term project


Project 1: features

Project 2: Lucas
-
Kande Tracker

http://www.youtube.com/watch?v=yoQ8pSXrl4g

Project 3: object detection

Term Project


Open
-
ended project of your choosing


Oral presentation


Midterm presentation


Final presentation and demo


Publish your results

General Comments


Prerequisites

these are essential
!


Data structures


A good working knowledge of MATLAB, C,
and C++ programming


Linear algebra


Vector calculus



Course does
not

assume prior imaging experience


computer vision, image processing, graphics,
etc.

Acknowledgements


Slides


David Forsyth and Jean Ponce


Richard Szleski and Steve Seitz