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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)[email protected]
• 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:
7Sebastian Thrun CS223B Computer Vision, Winter 2005
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)
15Sebastian Thrun CS223B Computer Vision, Winter 2005
Combined
Model Real Scene
Real Cameras
Image
Output
SyntheticCamera
(slides courtesy of Michael Cohen)
16Sebastian Thrun CS223B Computer Vision, Winter 2005
Example 1:Stereo
See http://schwehr.org/photoRealVR/example.html
17Sebastian Thrun CS223B Computer Vision, Winter 2005
Example 2: Structure From Motion
http://medic.rad.jhmi.edu/pbazin/perso/Research/SfMvideo.html
18Sebastian Thrun CS223B Computer Vision, Winter 2005
Example 3: 3D Modeling
http://www.photogrammetry.ethz.ch/research/cause/3dreconstruction3.html
19Sebastian Thrun CS223B Computer Vision, Winter 2005
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/
21Sebastian Thrun CS223B Computer Vision, Winter 2005
Example 5: Detection and Tracking
http://www.seeingmachines.com/facelab.htm
22Sebastian Thrun CS223B Computer Vision, Winter 2005
Example 6: Optical Flow
David Stavens, Andrew Lookingbill, David Lieb, CS223b Winter 2004
23Sebastian Thrun CS223B Computer Vision, Winter 2005
Example 7: Learning
Andrew Lookingbill, David Lieb, CS223b Winter 2004
Demo: Dirt Road
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
30Sebastian Thrun CS223B Computer Vision, Winter 2005
Pinhole Camera
*many slides in this lecture from Marc Pollefeys comp256, Lect 2
-- Brunelleschi, XVth Century
31Sebastian Thrun CS223B Computer Vision, Winter 2005
Perspective Projection
A “similar triangle’s” approach to vision. Notes 1.1
Marc Pollefeys
33Sebastian Thrun CS223B Computer Vision, Winter 2005
Consequences: Parallel lines meet
• There exist vanishing points
Marc Pollefeys
34Sebastian Thrun CS223B Computer Vision, Winter 2005
Vanishing points
VPL VPRH
VP1VP2
VP3
Different directions correspond to different vanishing points
Marc Pollefeys
36Sebastian Thrun CS223B Computer Vision, Winter 2005
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…
38Sebastian Thrun CS223B Computer Vision, Winter 2005
Weak Perspective Projection
f
Z
O-x
ZZ
XconstZ
fXx
Z
39Sebastian Thrun CS223B Computer Vision, Winter 2005
Generalization of Orthographic Projection
yY
xX When the camera is at a(roughly constant) distancefrom the scene, take m=1.
Marc Pollefeys
40Sebastian Thrun CS223B Computer Vision, Winter 2005
Pictorial Comparison
Weak perspective Perspective
Marc Pollefeys
41Sebastian Thrun CS223B Computer Vision, Winter 2005
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