Course Description:
CSE/EE 576 provides an overview of computer vision, emphasizing the middle ground between image processing and artificial intelligence. Low-level image processing, computational photography, motion and depth estimation, object recognition, and case studies of current research.
Office hours: There are no regularly scheduled office hours, but you can always arrange a meeting with either the TA or instructor. Just send an email.
Grading: Four programming assignments (10 base points each with various extra credits) and one final project (60 base points + 20 extra credits determined by the course staffs), each will be a mix of coding and written answers.
Books (optionals):
Computer Vision: A Modern Approach (2nd Edition), David A. Forsyth.
Computer Vision: Algorithms and Applications, Richard Szeliski.
Syllabus Overview:
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3/30
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4/1
- Images and Filters
- Image Sampling
- HW0: Setup your environment. Setup instructions.
- Decide on your final project
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4/6
- Edges
- Image Transformations
- Final Project Proposal Due [Submit to respective Dropbox]
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4/8
4/13
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4/15
- Descriptors 2
- Project Update 1 Due (11:45pm) [Submit to respective Dropbox]
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4/20
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4/22
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4/27
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4/29
- Stereo 2
- Project Update 2 Due (11:45pm) [Submit to respective Dropbox]
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5/4
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5/6
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5/11
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5/13
- Recognition 1
- Project Update 3 Due (11:45pm) [Submit to respective Dropbox]
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5/18
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5/20
5/25
- Memorial Day
- HW4: Due (11:45pm) [Submit to respective Dropbox]
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5/27
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6/1
- Object Detection2
- Wrap Up
- Project Update 4 Due (11:45pm) [Submit to respective Dropbox]
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6/3
- [TBD] Final Project Demo Due 1:30-3:30pm, CSE AC atrium (32"x40" Protrait) Printing info. You should be ready before 1:15pm.
- Final Presentation Information
6/8
- [TBD] Final Project Report Due 11:45pm (4 pages max) Format & template.[Submit to respective Dropbox].
Be sure to format your sections similar to the following: Abstract, Related Work, Framework, Result Discussion, Conclusion, and Bibliography.
Homework Notes:
Please upload your assignment in a compressed file including codes, executables, writing assignments, and the data required for the program. Also, please include a brief readme describing any extra credit (bells and whistles) you accomplished. Note that there is a deadline for each assignment. Anything uploaded after the deadline will be marked late.
Please be careful to not overwrite an in time assignment with a late assignment when uploading near the deadline.
Each student has four penalty-free late day for the whole quarter, other than that any late submission will be penalized for each day it is late, and no extra credit will be awarded.
Please let the TA know if you cannot access any of the pages.