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
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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 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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