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CS194-26/294-26: Image Manipulation, Computer Vision and Computational Photography
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![Description: [SCS dragon logo]](463_files/ucbseal_139_540.png)
INSTRUCTOR:
Alexei (Alyosha) Efros (Office hours: after lecture)
GSI: Ashish Kumar (Office hours: 5-6pm Wed at Soda Alcove-341B), Violet Fu (Office hours: 5pm-6pm Fri at Soda Alcove-341B), and Shivam Parikh (Office hours: 11am-1pm Mon at Cory 531).
UNIVERSITY UNITS: 4
SEMESTER: Spring 2020
WEB PAGE: http://inst.eecs.berkeley.edu/~cs194-26/fa20/
Q&A: Piazza Course Website
LOCATION: Hearst Field Annex A1
TIME
: TueThu 5:00 PM-6:30
PM
MIDTERM: April 16th, Thurs, during the class.
PREREQUISITES:
This is a heavily project-oriented class, therefore good programming proficiency (at least CS61B) is absolutely essential.
Moreover, familiarity with linear algebra (MATH 54 or EE16A/B or Gilbert Strang's online class) and calculus
are vital. Experience with neural networks (e.g. CS189) is a plus.
For these taking CS294-26, consent of instructor is required to register (please sign up on the waitlist first).
COURSE
DESCRIPTION:
The aim of this advanced undergraduate course is to introduce students to computing with visual data (images and video).
We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing),
image analysis and visual understanding (computer vision), and image synthesis (computational photography).
Key algorithms will be presented, ranging from classical (e.g. Gaussian and Laplacian Pyramids) to contemporary (e.g. ConvNets, GANs),
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 and develop, largely from scratch, the image analysis and synthesis tools for solving applications.
PROGRAMMING ASSIGNMENTS:
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Project 1: Images of the
Russian Empire -- colorizing the Prokudin-Gorskii
photo collection
Project 2: Fun with Filters and Frequencies
Project 3: Face Morphing and Modelling a Photo Collection
 Class Choice Awards:
Zhimin Cai Also, see the class morph video.
Project 4: Classification and Segmentation
Class Choice Awards:
Andrew Lee
Project 5: (Auto)stitching and photo mosaics
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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:
Computer Vision: A Modern Approach
(2nd edition), Forsyth and Ponce (classic computer vision text)
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)
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:
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CLASS DATE |
TOPICS |
Material |
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Tues Jan 21 |
Introduction |
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Thurs
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Capturing Light... in man and machine |
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Tues
Jan 28 |
The Camera |
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Tues/Thurs
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Sampling and Reconstruction |
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Tues |
Sampling and Reconstruction |
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Thurs |
Derivative and Template Filters |
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Tues
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Image Blending and Compositing |
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Thurs
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Compression and Gradient Domain |
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Tues
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Edge Detection and Image Warping |
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Thurs
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Image Morphing |
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Feb 25
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Data-driven Methods: Faces |
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Feb 27, Mar 3
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Data-driven Methods: Video Textures |
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Thurs
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Feature Learning with Neural Networks |
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Tues, Thurs
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Convolutional Neural Networks |
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Mar 17, 19 |
Modeling Light |
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Mar 31, Apr 7 |
Homographies and Mosaics |
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Apr 9 |
Automatic Alignment |
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Tues, Thurs
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Scene Modeling for a Single View |
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Tues, Thurs
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Multiview Geometry: Stereo & Structure from Motion |
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Tues, Thurs
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Class chosen special topics |
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 + several in-class pop quizzes (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:
- Computational Photography (Hoiem, UIUC)
- Computational Photography (Hays,
Brown)
- Digital and Computational Photography
(Durand, MIT)
- Computer Vision (Seitz &
Szeliski, UWashington)
Page design
courtesy of Doug James