1Sebastian Thrun CS223B Computer Vision, Winter 2005

Stanford CS223B Computer Vision, Winter 2005

Lecture 1 Intro and Image Formation

Sebastian Thrun, Stanford

Rick Szeliski, Microsoft

Hendrik Dahlkamp, Stanford

2Sebastian Thrun CS223B Computer Vision, Winter 2005

Today’s Goals

• Learn about CS223b

• Get Excited about Computer Vision

• Learn about Image Formation (tbc)

3Sebastian Thrun CS223B Computer Vision, Winter 2005

Administrativa

• Time and LocationTue/Thu 1:15-2:35, Gates B03SCPD Televised (Live on Channel E5)

• Web sitehttp://cs223b.cs.stanford.edu

Class Email list (announcements only)cs223b@cs.stanford.edu

• Class newsgroup (discussion)su.class.cs223b (server: news.stanford.edu)

4Sebastian Thrun CS223B Computer Vision, Winter 2005

People Involved

• You! (63 students)

• Me!

• Rick Szeliski, Microsoft

• Hendrik Dahlkamp:

5Sebastian Thrun CS223B Computer Vision, Winter 2005

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The Text

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Course Overview

• Basics– Image Formation and Camera Calibration– Image Features

• 3D Reconstruction– Stereo– Image Mosaics

• Motion– Optical Flow– Structure From Motion– Tracking

• Object detection and recognition– Grouping– Detection– Segmentaiton– Classification

8Sebastian Thrun CS223B Computer Vision, Winter 2005

Course Outline

• http://cs223b.stanford.edu/schedule.html

9Sebastian Thrun CS223B Computer Vision, Winter 2005

Goals

• To familiarize you with basic the techniques and jargon in the field

• To enable you to solve computer vision problems

• To let you experience (and appreciate!) the difficulties of real-world computer vision

• To get you excited!

10Sebastian Thrun CS223B Computer Vision, Winter 2005

Requirements• Attend + participate in all classes except at

most two• Turn in all assignments (even if for zero

credit)• Pass the midterm exam • Successfully carry out research project

– Jan 31: selection– Feb 14: Interim report– March 8/10: Class presentation– March 15: Final report

• No exceptions!

11Sebastian Thrun CS223B Computer Vision, Winter 2005

Grading Criteria

• 10% Participation

• 30% Assignments

• 30% Midterm exam

• 30% Project

(35% of all students received an A in CS223b-04)

12Sebastian Thrun CS223B Computer Vision, Winter 2005

Today’s Goals

• Learn about CS223b

• Get Excited about Computer Vision

• Learn about image formation (tbc)

13Sebastian Thrun CS223B Computer Vision, Winter 2005

Computer Graphics

Image

Output

ModelSyntheticCamera

(slides courtesy of Michael Cohen)

14Sebastian Thrun CS223B Computer Vision, Winter 2005

Real Scene

Computer Vision

Real Cameras

Model

Output

(slides courtesy of Michael Cohen)

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Combined

Model Real Scene

Real Cameras

Image

Output

SyntheticCamera

(slides courtesy of Michael Cohen)

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Example 1:Stereo

See http://schwehr.org/photoRealVR/example.html

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Example 2: Structure From Motion

http://medic.rad.jhmi.edu/pbazin/perso/Research/SfMvideo.html

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Example 3: 3D Modeling

http://www.photogrammetry.ethz.ch/research/cause/3dreconstruction3.html

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Example 4: Classification

http://elib.cs.berkeley.edu/photos/classify/

20Sebastian Thrun CS223B Computer Vision, Winter 2005

Example 4: Classification

http://elib.cs.berkeley.edu/photos/classify/

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Example 5: Detection and Tracking

http://www.seeingmachines.com/facelab.htm

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Example 6: Optical Flow

David Stavens, Andrew Lookingbill, David Lieb, CS223b Winter 2004

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Example 7: Learning

Andrew Lookingbill, David Lieb, CS223b Winter 2004

Demo: Dirt Road

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Example 8: Human Vision

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Example 8: Human Vision

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Excited Yet?

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Computer Vision [Trucco&Verri’98]

28Sebastian Thrun CS223B Computer Vision, Winter 2005

Today’s Goals

• Learn about CS223b

• Get Excited about Computer Vision

• Learn about image formation (tbc)

29Sebastian Thrun CS223B Computer Vision, Winter 2005

Topics

• Pinhole Camera

• Orthographic Projection

• Perspective Camera Model

• Weak-Perspective Camera Model

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Pinhole Camera

*many slides in this lecture from Marc Pollefeys comp256, Lect 2

-- Brunelleschi, XVth Century

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Perspective Projection

A “similar triangle’s” approach to vision. Notes 1.1

Marc Pollefeys

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Perspective Projection

x

fZ Z

fXx

XO

-x

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Consequences: Parallel lines meet

• There exist vanishing points

Marc Pollefeys

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Vanishing points

VPL VPRH

VP1VP2

VP3

Different directions correspond to different vanishing points

Marc Pollefeys

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The Effect of Perspective

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Implications For Perception*

* A Cartoon Epistemology: http://cns-alumni.bu.edu/~slehar/cartoonepist/cartoonepist.html

Same size things get smaller, we hardly notice…

Parallel lines meet at a point…

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Perspective Projection

fZ Z

fXx

XO

-x

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Weak Perspective Projection

f

Z

O-x

ZZ

XconstZ

fXx

Z

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Generalization of Orthographic Projection

yY

xX When the camera is at a(roughly constant) distancefrom the scene, take m=1.

Marc Pollefeys

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Pictorial Comparison

Weak perspective Perspective

Marc Pollefeys

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camera theoflength focal

depth

scoordinate world,,

scoordinate image,

f

Z

ZYX

yx

Summary: Perspective Laws

1. Perspective

2. Weak perspective

3. OrthographicYconstyXconstx

Z

fYy

Z

fXx

YyXx

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Limits for pinhole cameras