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CS194-26: Image Manipulation and Computational
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INSTRUCTOR:
Alexei (Alyosha) Efros (Office hours: Wed 11am-12pm at 724 Sutardja Dai Hall)
GSI: Taesung Park (Office hours: 4-5pm Thurs at Soda-Alcove-341A) and Shiry Ginosar (Office hours: 10am-11am Tuesdays at one of the long tables at Sutardja Dai Hall 2nd Floor Yali's).
UNIVERSITY UNITS: 4
SEMESTER: Fall 2018
WEB PAGE: http://inst.eecs.berkeley.edu/~cs194-26/fa18/
Q&A: Piazza Course Website
LOCATION: Valley Life Sciences Building (VLSB) 2060
TIME
: TueThu 5:00 PM-6:30
PM
MIDTERM: Nov 13th Tue in-class.
PREREQUISITES:
Programming experience (CS61B) and familiarity with linear algebra (MATH 54 or EE16A/B or Strang's online class) and calculus is
assumed. Some background in computer
graphics, computer vision, or image processing is helpful. This class does not significantly overlap
with cs184 (Computer Graphics) and can be taken concurrently.
Note: if the system doesn't let you
sign up, or puts you on the waitlist, do talk to me.
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:
PROGRAMMING ASSIGNMENTS:
Project 1: Images of the
Russian Empire -- colorizing the Prokudin-Gorskii
photo collection
Class Choice Awards: Zeyana Musthafa
Project 2: Building a Pinhole Camera
Class Choice Awards:
Andrew Campbell
Project 3: Fun with Frequencies and Gradients
Class Choice Awards:
Andrew Campbell Project 4: Face Morphing and Modelling a Photo Collection Project 5: Depth Refocusing and Aperture Adjustment with Lightfield data
See student submissions here Class Choice Awards: Eli Lipsitz
Project 6: (Auto)stitching and photo mosaics See student submissions here Class Choice Awards: Andrew Campbell See student submissions (pre-canned projects) here See student submissions (own proposed projects) here |
TEXT:
There is a textbook that covers most (if not all) of the
topics related to Computational Photography. This will be the primary reference for
the course:
Computer
Vision: Algorithms and Applications, Richard Szeliski,
2010
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)
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.
TENTATIVE CLASS SCHEDULE:
CLASS DATE |
TOPICS |
Material |
Thurs Aug 23 |
Introduction |
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Tues/Thurs
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Capturing Light... in man and machine |
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Tues/Thurs
Aug 30, Sept 4 |
The Camera |
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Tues/Thurs
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Sampling and Reconstruction |
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Thurs |
Sampling and Reconstruction |
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Tues, Thurs
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Image Blending and Compositing |
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Tues, Thurs
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Point Processing and Image Warping |
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Thurs, Tues
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Image Morphing |
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Sept 27, Oct 2, 4
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Data-driven Methods: Faces |
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Oct 9
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Data-driven Methods: Video Textures |
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Oct 11
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Data-driven Methods: Visual Data on the Internet |
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Oct 16 |
Deep Learning in Computational Photography |
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Oct 18, 23 |
Modeling Light |
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Oct 25 |
Homographies and Mosaics |
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Oct 30, Nov 1 |
More Mosaic Madness |
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Nov 6, 8 |
Automatic Alignment |
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Nov 8 |
Multi-perspective Panoramas |
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Tues Nov 13 |
2/3rds-term exam! |
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Nov 28 |
Image-based Lighting |
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What makes a great picture? |
CAMERAS:
Although it is not required, students are highly encouraged to obtain a digital
camera for use in the course.
METHOD OF EVALUATION:
Grading will be based on a set of programming and written assignments (60%), a midterm
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 CS294-26 will also be required to
submit a conference-style paper describing their final project.
PROGRAMMING RESOURCES:
Students will be encouraged to use either MATLAB (with the Image Processing Toolkit) or Python (with either scikit-image or opencv) as their primary computing platform. Specific libraries in both languages offer tons of build-in image processing
functions. Here is a link to some useful MATLAB and Python resources compiled for this class.
PREVIOUS OFFERINGS OF THIS COURSE:
Previous offerings of this course can be found here.
SIMILAR COURSES IN OTHER UNIVERSITIES:
Page design
courtesy of Doug James