Stanford CS223B Computer Vision, Winter 2005 Lecture 1 Intro and Image Formation

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Stanford CS223B Computer Vision, Winter
2005Lecture 1 Intro and Image Formation

• Sebastian Thrun, Stanford
• Rick Szeliski, Microsoft
• Hendrik Dahlkamp, Stanford

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Todays Goals

• Learn about CS223b
• Get Excited about Computer Vision
• Learn about Image Formation (tbc)

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Administrativa

• Time and Location
• Tue/Thu 115-235, Gates B03
• SCPD Televised (Live on Channel E5)
• Web site
• http//cs223b.cs.stanford.edu
• Class Email list (announcements only)
• cs223b_at_cs.stanford.edu
• Class newsgroup (discussion)
• su.class.cs223b (server news.stanford.edu)

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People Involved

• You! (63 students)
• Me!
• Rick Szeliski, Microsoft
• Hendrik Dahlkamp

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(No Transcript)
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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

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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)

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Todays Goals

• Learn about CS223b
• Get Excited about Computer Vision
• Learn about image formation (tbc)

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Computer Graphics
Output
Image
Model
Synthetic Camera
(slides courtesy of Michael Cohen)
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Computer Vision
Output
Model
Real Scene
Real Cameras
(slides courtesy of Michael Cohen)
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Combined
Output
Image
Real Scene
Model
Synthetic Camera
Real Cameras
(slides courtesy of Michael Cohen)
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Example 1Stereo
See http//schwehr.org/photoRealVR/example.html
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Example 2 Structure From Motion
http//medic.rad.jhmi.edu/pbazin/perso/Research/Sf
Mvideo.html
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Example 3 3D Modeling
http//www.photogrammetry.ethz.ch/research/cause/3
dreconstruction3.html
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Example 4 Classification
http//elib.cs.berkeley.edu/photos/classify/
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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
Demo Dirt Road
Andrew Lookingbill, David Lieb, CS223b Winter 2004
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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 TruccoVerri98
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Todays Goals

• Learn about CS223b
• Get Excited about Computer Vision
• Learn about image formation (tbc)

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Topics

• Pinhole Camera
• Orthographic Projection
• Perspective Camera Model
• Weak-Perspective Camera Model

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Pinhole Camera
-- Brunelleschi, XVth Century
many slides in this lecture from Marc Pollefeys
comp256, Lect 2
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Perspective Projection
A similar triangles approach to vision. Notes
1.1
Marc Pollefeys
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Perspective Projection
O
X
-x
f
Z
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Consequences Parallel lines meet

• There exist vanishing points

Marc Pollefeys
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Vanishing points
VPL
H
VPR
VP2
VP1
Different directions correspond to different
vanishing points
VP3
Marc Pollefeys
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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
O
X
-x
f
Z
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Weak Perspective Projection
Z
O
-x
Z
f
Z
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Generalization of Orthographic Projection
When the camera is at a (roughly constant)
distance from the scene, take m1.
Marc Pollefeys
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Pictorial Comparison
Weak perspective
Perspective
?
Marc Pollefeys
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Summary Perspective Laws

1. Perspective
2. Weak perspective
3. Orthographic

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Limits for pinhole cameras