CS 223-B Lecture 1

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Transcript CS 223-B Lecture 1

Stanford CS223B Computer Vision, Winter 2005 Lecture 1

Intro and Image Formation

Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik Dahlkamp, Stanford Sebastian Thrun CS223B Computer Vision, Winter 2005 1

Today’s Goals

• Learn about CS223b • Get Excited about Computer Vision • Learn about Image Formation (tbc) Sebastian Thrun CS223B Computer Vision, Winter 2005 2

Administrativa

• Time and Location Tue/Thu 1:15-2:35, Gates B03 SCPD Televised (Live on Channel E5) • Web site http://cs223b.cs.stanford.edu

Class Email list (announcements only) [email protected]

• Class newsgroup (discussion) su.class.cs223b (server: news.stanford.edu) Sebastian Thrun CS223B Computer Vision, Winter 2005 3

People Involved

• You! (63 students) • Me!

• Rick Szeliski, Microsoft • Hendrik Dahlkamp: Sebastian Thrun CS223B Computer Vision, Winter 2005 4

Sebastian Thrun CS223B Computer Vision, Winter 2005 5

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 Sebastian Thrun CS223B Computer Vision, Winter 2005 7

Course Outline

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

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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!

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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!

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Grading Criteria

• 10% Participation • 30% Assignments • 30% Midterm exam • 30% Project (35% of all students received an A in CS223b-04) Sebastian Thrun CS223B Computer Vision, Winter 2005 11

Today’s Goals

• Learn about CS223b • Get Excited about Computer Vision • Learn about image formation (tbc) Sebastian Thrun CS223B Computer Vision, Winter 2005 12

Computer Graphics

Output Image Synthetic Camera Model (slides courtesy of Michael Cohen) Sebastian Thrun CS223B Computer Vision, Winter 2005 13

Computer Vision

Output Model Real Scene Real Cameras (slides courtesy of Michael Cohen) Sebastian Thrun CS223B Computer Vision, Winter 2005 14

Combined

Output Image Synthetic Camera Model Real Scene Real Cameras (slides courtesy of Michael Cohen) Sebastian Thrun CS223B Computer Vision, Winter 2005 15

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/ Sebastian Thrun CS223B Computer Vision, Winter 2005 19

Example 4: Classification

http://elib.cs.berkeley.edu/photos/classify/ Sebastian Thrun CS223B Computer Vision, Winter 2005 20

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 Sebastian Thrun CS223B Computer Vision, Winter 2005 22

Example 7: Learning

Demo: Dirt Road Andrew Lookingbill, David Lieb, CS223b Winter 2004 Sebastian Thrun CS223B Computer Vision, Winter 2005 23

Example 8: Human Vision Sebastian Thrun CS223B Computer Vision, Winter 2005 24

Example 8: Human Vision Sebastian Thrun CS223B Computer Vision, Winter 2005 25

Excited Yet?

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Computer Vision [ Trucco&Verri’98] Sebastian Thrun CS223B Computer Vision, Winter 2005 27

Today’s Goals

• Learn about CS223b • Get Excited about Computer Vision • Learn about image formation (tbc) Sebastian Thrun CS223B Computer Vision, Winter 2005 28

Topics

• Pinhole Camera • Orthographic Projection • Perspective Camera Model • Weak-Perspective Camera Model Sebastian Thrun CS223B Computer Vision, Winter 2005 29

Pinhole Camera

-- Brunelleschi, XVth Century *many slides in this lecture from Marc Pollefeys comp256, Lect 2 Sebastian Thrun CS223B Computer Vision, Winter 2005 30

Perspective Projection

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

Sebastian Thrun CS223B Computer Vision, Winter 2005

Perspective Projection

X Z x O f -x x

X f Z

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Consequences: Parallel lines meet • There exist vanishing points Sebastian Thrun CS223B Computer Vision, Winter 2005

Vanishing points

H VPL VPR VP 1 Different directions correspond to different vanishing points VP 3 Sebastian Thrun CS223B Computer Vision, Winter 2005 VP 2 Marc Pollefeys 34

The Effect of Perspective

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Implications For Perception*

Same size things get smaller, we hardly notice… Parallel lines meet at a point… * A Cartoon Epistemology: http://cns-alumni.bu.edu/~slehar/cartoonepist/cartoonepist.html

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Perspective Projection

X Z O f -x x

X f Z

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Weak Perspective Projection

Z

Z Z Z O f -x x

 

f X Z const

X

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Generalization of Orthographic Projection

X

Y

 

y x

When the camera is at a (roughly constant) distance from the scene, take

m

=1.

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Pictorial Comparison

Weak perspective Perspective

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Summary: Perspective Laws

1. Perspective

x

f X Z

2. Weak perspective

x

const

3. Orthographic

x

X X y

f Y Z y

const Y y

Y X

,

Y x

,

y

 image coordinate s ,

Z Z f

 world coordinate s  depth  focal length of the camera Sebastian Thrun CS223B Computer Vision, Winter 2005 41

Limits for pinhole cameras

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