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15-463 (15-862):
Computational |
![[SCS dragon logo]](scslogo.gif)
INSTRUCTOR: Alexei (Alyosha) Efros (Office hours: Tu
5-6pm, NSH 4207)
TA: Santosh Kumar Divvala (Office hours: Wed
3-4:30pm, EDSH 200)
UNIVERSITY
UNITS: 12
SEMESTER: Fall 2008
NEWSGROUP: http://flickr.com/groups/15-463/ (need to join Flickr)
WEB
PAGE: http://graphics.cs.cmu.edu/courses/15-463/
LOCATION: BH 237B
TIME: T R 12:00--1:20 PM
COURSE OVERVIEW:
Computational Photography is an emerging new field created by the convergence
of computer graphics, computer vision and photography. Its role is to overcome the
limitations of the traditional camera by using computational techniques to
produce a richer, more vivid, perhaps more perceptually meaningful
representation of our visual world.
The aim of this advanced
undergraduate course is to study ways in which samples from the real world
(images and video) can be used to generate compelling computer graphics
imagery. We will learn how to acquire, represent, and render scenes from
digitized photographs. Several popular image-based algorithms will be
presented, with an emphasis on using these techniques to build practical
systems. This hands-on emphasis will be reflected in the programming
assignments, in which students will have the opportunity to acquire their own
images of indoor and outdoor scenes and develop the image analysis and
synthesis tools needed to render and view the scenes on the computer.
TOPICS TO BE COVERED:
- Cameras, Image Formation
- Visual Perception
- Image and Video Processing (filtering,
anti-aliasing, pyramids)
- Image Manipulation (warping, morphing, mosaicing,
matting, compositing)
- Modeling and Synthesis using Lots of Data
- Image-Based Lighting
- Image-Based
Rendering
- Non-photorealistic
Rendering
PREREQUISITES:
Programming experience and familiarity with linear algebra and calculus is
assumed. Some background in computer
graphics, computer vision, or image processing is helpful. This class does not significantly overlap with
15-462 and can be taken concurrently.
Graduate Students: a small number of graduate students will be allowed
to take the graduate version of this course (15-862) with the permission of the
instructor. Students taking 15-862 will be required to do more substantial
assignments as well as a research-level final paper.
Note: if the system doesn’t
let you sign up, or puts you on the waitlist, do talk to me.
PROGRAMMING ASSIGNMENTS:
· Project
0: The Vertigo Shot
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Project 1: Images of the Russian Empire -- colorizing the
Prokudin-Gorskii photo collection Class Choice Award: Frank
Palermo |
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Project 2: Image Resizing by Seam Carving |
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· Project 3: Face morphing and modeling: See
results |
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Project 4: Stitching Photo Mosaics (including autostitching) |
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· Project 5: Single-View Reconstruction(Tour into the Picture): |
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Final Projects! |
TEXT:
Since Computational Photography is such a new discipline, no comprehensive
textbook exists for use in the class. Therefore, there is no required text.
Various course notes and papers will be made available. Furthermore, there is an optional textbook
that you might find helpful. It will be
placed on reserve at the Wean Hall library:
Computer
Vision: The Modern Approach, Forsyth and
There is a number of other fine
texts that you can use for general reference:
Photography (8th
edition), London and Upton, (a great
general guide to taking pictures)
Vision Science: Photons to
Phenomenology, Stephen Palmer (great book
on human visual perception)
Digital Image Processing, 2nd edition, Gonzalez and
Woods (a good general image processing
text)
The Art and Science of Digital Compositing, Ron Brinkmann (everything about compositing)
Multiple View Geometry in
Computer Vision, Hartley & Zisserman (a
bible on recovering 3D geometry) [on reserve]
The Computer Image, Watt and Policarpo (a
nice “vision for graphics” text, somewhat dated)
3D Computer Graphics (3rd Edition), Watt (a
good general graphics text)
Fundamentals of Computer Graphics, Peter Shirley (another good general graphics text)
Linear Algebra and its Applications, Gilbert Strang (a truly wonderful book on linear algebra)
CLASS NOTES
The instructor is extremely grateful to a large number of researchers for
making their slides available for use in this course. Steve Seitz and Rick Szeliski have been
particularly kind in letting me use their wonderful lecture notes. In addition, I would like to thank Paul Debevec, Stephen Palmer, Paul Heckbert, David Forsyth, Steve Marschner and others, as noted
in the slides. The instructor gladly
gives permission to use and modify any of the slides for academic and research
purposes. However, please do also acknowledge the original sources where appropriate.
CLASS SCHEDULE:
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CLASS DATE |
TOPICS |
Material |
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Tu
Aug 26 |
Introduction |
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Th Aug 28 |
The Camera |
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Tu Sept 2 |
Capturing Light… in man and machine |
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Th Sept 4 |
Sampling and Reconstruction |
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Tu Sept 9 |
The Frequency Domain |
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Th Sept 11 |
Continue with Frequency Domain |
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Tu Sept 16 |
Image Blending and Compositing |
Additional |
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Th Sept 18 |
Point Processing |
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Fri Sept 19 |
Relevant Talk: Jim McCann,
Gradient Image Editing, 5409 Wean Hall, Noon |
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Tu Sept 23 |
Image Warping |
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Th Sept 25 |
Image Morphing |
Slides (ppt, pdf) |
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Tu Sept 30 |
Data-driven Methods: Faces |
Slides (ppt, pdf) · Additional
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Th Oct 2 |
Data-driven Methods: Video and Texture |
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Tu Oct 7 |
Data-driven Methods: Features, Histograms,
and Image Comparisons |
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Th Oct 9 |
Data-driven Methods: Lots of Images (by James Hays) + midterm review (by
Santosh) |
Additional |
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Tu Oct 14 |
Midterm Exam |
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Th Oct 16 |
No class (ECCV) |
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Tu Oct 21 |
Data-Driven Methods: guest lecture by Jean-Francois Lalonde |
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Tu Oct 23 |
Modeling
Light |
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Th Oct 28 |
Homographies
and Mosaics |
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Tu Oct 30 |
More
Mosaic Madness |
·
Additional
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Tu Nov 4 |
Automatic
Alignment |
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Th Nov 6 |
Multi-perspective
Panoramas |
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Tu Nov 11 |
Single
View Reconstruction |
· |
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Th Nov 13 |
More
Single View Geometry |
· Additional |
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Tu Nov 18 |
Matting |
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Th Nov 20 |
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Tu Nov 28 |
Image-based
Lighting |
Slides (ppt, pdf) |
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Tu Dec 4 |
Image-based
Lighting II |
Slides (ppt,
pdf) |
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Th Dec 6 |
What makes a Great Picture? +
Wrap-up |
Slides (ppt, pdf) |
CAMERAS:
Although it is not required, students are highly encouraged to obtain a digital
camera for use in the course (one can get a pretty
good camera for under $150). A camera might be available on load from the
instructor.
METHOD OF EVALUATION:
Grading will be based on a set of programming and written assignments (60%), an
exam (20%) and a final project (20%).
For the programming assignments, students will be allowed a total of 5
(five) late days per semester; each additional late day will incur a 10%
penalty.
Students taking 15-862 will also be required to submit a
conference-style paper describing their final project.
COMPUTING
FACILITIES:
All students will have access to
the graphics cluster in Wean Hall 5336. You will need to setup your Andrew
account in the right way before you can login.
MATLAB:
Students will be encouraged to
use Matlab (with the Image Processing Toolkit) as their primary computing
platform. Besides being a great
prototyping environment, Matlab is particularly well-suited for working with
image data and offers tons of build-in image processing functions. Here is a link to some useful
Matlab resources
PREVIOUS OFFERINGS OF THIS
COURSE:
Previous offerings of this course can be found here.
SIMILAR
COURSES IN OTHER UNIVERSITIES:
- Computational Photography
SIGGRAPH Course (Raskar & Tumblin)
- Computational Camera and
Photography (Raskar, MIT Media Lab)
- Digital
and Computational Photography (Durand & Freeman, MIT)
- Computational
Photography (
- Computational
Photography (Levoy, Adams, Pulli, Stanford)
- Computational
Photography (Lazebnik, UNC)
- Computational
Photography (Fergus, NYU)
- Internet
Vision (T.Berg, SUNY)
- Computer Vision (Seitz & Szeliski, UWashington)
- Instroduction to
Visual Computing and Visual Modeling
(Kutulakos, UToronto)
- Symposium on Computational Photography and
Video (May 2005, MIT)
Page design courtesy of Doug James