Transcript Introduction - Computer Science Department

3D Computer Vision: CSc 83020
3D Computer Vision: CSc 83020
Instructor: Ioannis Stamos
istamos (at) hunter.cuny.edu
http://www.cs.hunter.cuny.edu/~ioannis
Office Hours: Tuesdays 4-6 (at Hunter) or by appoitnment
Office: 1090G Hunter North (69th street bw. Park and Lex.)
Computer Vision Lab: 1090E Hunter North
Course web page: http://www.cs.hunter.cuny.edu/~ioannis/3D_f12.html
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!
Class Policy
• You have to
– Turn in all assignments (60% of grade)
– Complete a final project (30% of grade)
– Actively participate in class (10% of grade)
• Late policy
– Six late days (but not for final project)
• Teaming
– For final project you can work in groups of 2
About me
• 11th year at Hunter and the Graduate
Center
• Graduated from Columbia in ’01
– CS Ph.D.
• Research Areas:
– Computer Vision
– 3D Modeling
– Computer Graphics
Books
Computer Vision: Algorithms and Applications, Richard Szeliski, 2010 (available
online for free)
Robot Vision
B. K. P. Horn, The MIT Press (great classic book)
Introductory Techniques for 3-D Computer Vision
Emanuele Trucco and Alessandro Verri, Prentice Hall, 1998 (algorithmic
perspective)
Computer Vision A Modern Approach
David A. Forsyth, Jean Ponce, Prentice Hall 2003
An Invitation to 3-D Vision
Yi Ma, Stefano Soatto, Jana Kosecka, S. Shankar Sastry
Springer 2004.
Three-Dimensional Computer Vision: A Geometric Viewpoint Olivier Faugeras
The MIT Press, 1996.
Journals/Web
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International Journal of Computer Vision.
Computer Vision and Image Understanding.
IEEE Trans. on Pattern Analysis and Machine Intelligence.
SIGGRAPH (mostly Graphics)
http://www.ri.cmu.edu/ (CMU’s Robotic Institute)
http://www.cs.cmu.edu/~cil/vision.html
(The Vision Home Page)
• http://www.dai.ed.ac.uk/CVonline/
(CV Online)
• http://iris.usc.edu/Vision-Notes/bibliography/contents.html
(Annotated CV Bibliography)
Class History
• Based on class taught at Columbia University
by Prof. Shree Nayar.
• New material reflects modern approach.
• Taught similar class at Hunter
• Taught “3D Photography” class at the Graduate Center of
CUNY.
• My active research area
– Funded by the National Science Foundation
Class Schedule
• Check class website
• Final project proposals
– Due Nov. 7
– Design your own or check list of possible
projects on class website
• Final project presentations and report
– May 16 (last class)
What is Computer Vision?
Sensors
Images or Video
Illumination
Vision System
Physical 3D World
Scene Description
Measuring Visual Information
Computer Graphics
Output
Image
Synthetic
Camera
Model
(slides courtesy of Michael Cohen)
Computer Vision
Output
Model
Real Scene
Real Cameras
(slides courtesy of Michael Cohen)
Combined
Output
Image
Model
Synthetic
Camera
Real Scene
Real Cameras
(slides courtesy of Michael Cohen)
Cont.
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Vision is automating visual processes (Ball & Brown).
Vision is an information processing task (Marr).
Vision is inverting image formation (Horn).
Vision is inverse graphics.
Vision looks easy, but is difficult.
Vision is difficult, but it is fun (Kanade).
Vision is useful.
Some Applications
• Industrial
– Material Handling
– Inspection
– Assembly
Some Applications
Autonomous Navigation
Some Applications
Vision for Graphics
Film Industry
Urban Planning
E-commerce
Virtual Reality
Some Applications
• Realistic 3D experience
– Google Earth
http://earth.google.com/
– Microsoft Photosynth
http://labs.live.com/photosynth/
More Applications!
• Optical Character Recognition (OCR)
• Visual Databases (images or movies)
– Searching for image content
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Face Recognition (security)
Iris Recognition (security)
Traffic Monitoring Systems
Many more…
Vision deals with images
Images Look Nice…
Images Look Nice…
Ioannis Stamos – CSc 83020 Spring 2007
...Essentially a 2D array of numbers
107 132 107 107 132 99 132 107 132 99 107 132 99 107 132 91 107
132 99 132 99 107 107 132 99 132 107 132 107 132 91 107 132 107
132 99 107 132 107 132 107 99 132 99 132 99 132 99 132 124 132
99 132 107 132 132 107 132 124 132 132 124 132 150 107 150 150 132
150 132 150 132 150 107 150 132 124 132 132 150 107 99 132 132 107
132 107 132 150 132 150 99 132 107 150 132 107 150 132 124 132 132
107 150 99 150 107 150 132 107 150 132 124 132 150 115 124 132 150
107 132 150 132 150 150 107 132 116 132 124 132 107 99 150 132 107
132 150 132 124 132 150 107 150 107 132 99 132 107 150 132 150 107
150 132 150 150 107 107 150 150 150 150 115 167 107 150 107 132 150
107 150 132 124 132 124 132 124 132 124 132 150 107 150 107 107 132
116 132 150 132 150 107 150 150 132 150 132 116 132 124 132 150 132
150 150 150 132 116 132 116 107 132 99 150 150 132 107 132 150 107
150 132 124 132 116 132 107 150 132 107 150 132 150 107 150 107 132
Low-Level or “Early” Vision
• Considers local
properties of an
image
“There’s an edge!”
From: Szymon Rusinkiewicz, Princeton.
Mid-Level Vision
• Grouping and
segmentation
“There’s an object
and a background!”
High-Level Vision
• Recognition
“It’s a chair!”
Humans
Vision is easy for us.
But how do we do it?
Human Vision: Illusions
Fraser’s spiral (Fraser 1908)
Illusions
Zölner Illusion (1860)
Hering Illusion (1861)
Wundt Illusion (1896)
Visual Ambiguities
Young-Girl/Old-Woman
Visual Ambiguities
Visual Ambiguities
Seeing and Thinking
Kanizsa (1979)
Syllabus Overview
Image Formation and Optics
Light Source
p
Surface normal
CCD Array
Lens
P
Object Surface
Projection of 3-D World on a 2-D plane
Lenses
Ray of light
Optical Axis
Image Sensors/Camera Models
Typical 512x512 CCD array
Imaging Area 262,144 pixels
One Pixel
20μm
20μm
Convert Optical Images
To Electrical Signals.
512 (10.25mm)
Filtering

=
g
f
g i, j    f (u, v)h(i  u, j  v)
u
v
h
Image Features
Detecting intensity changes in the image
Ioannis Stamos – CSc 83020 Spring 2007
Grouping image features
Finding continuous lines from edge segments
Ioannis Stamos – CSc 83020 Spring 2007
Camera Calibration
Camera Coordinate Frame
Zc
Pixel Coordinates
Yc
Xc
Extrinsic
Parameters
Zw
Yw
World Coordinate Frame
Xw
Intrinsic
Parameters
Image Coordinate Frame
Shape from X
• Shape from X
– Stereo
– Motion
– Shading
– Texture foreshortening
Binocular Stereo
depth map
Active Sensing
Sources of error:
1) grazing angle,
2) object boundaries.
Sheet of
light
Lens
CCD
array
Shape from Shading
Three-dimensional shape from a single image.
Ioannis Stamos – CSc 83020 Spring 2007
Motion (optical flow)
Determining the movement of scene objects
Ioannis Stamos – CSc 83020 Spring 2007
Reflectance and Color
Why do these spheres look different?
Object Recognition
Learning visual appearance.
Real-time object recognition.
Template-Based Methods
Cootes et al.
Some Vision Systems…
Example 2: Structure From Motion
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
http://www.cs.unc.edu/Research/urbanscape
Example 2: Structure From Motion
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
http://www.cs.unc.edu/Research/urbanscape
Example 2: Structure From Motion
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
http://www.cs.unc.edu/Research/urbanscape
Example 2: Structure From Motion
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
http://www.cs.unc.edu/Research/urbanscape
Example 2: Structure From Motion
http://www.cs.unc.edu/Research/urbanscape
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
Example 4: 3D Modeling
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
Drago Anguelov
Example 6: Classification
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
Example 6: Classification
Slide courtesy of
Sebastian Thrun
http://cs223b.stanford.edu
Stanford
Real-world Applications
Osuna et al:
Range Scanning Outdoor Structures
Ioannis Stamos – CSc 83020 Spring 2007
Data Acquisition
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Spot laser scanner.
Time of flight.
Max Range: 100m.
Scanning time:
20 minutes for
1000 x1000 points.
• Accuracy: 6mm.
Video
Latest Video
Inserting models in Google Earth
Dynamic Scenes
Image sequence (CMU, Virtualized Reality Project)
Dynamic Scenes
Dynamic 3D model.
Dynamic Scenes
Dynamic texture-mapped model.
Scanning the David
Marc Levoy, Stanford
height of gantry:
weight of gantry:
7.5 meters
800 kilograms
Head of Michelangelo’s
David
photograph
1.0 mm computer model
What do you think?