CSE 473/573 - Computer Vision and Image Processing
TuTh 9:30AM - 10:50AM
Cooke 121
Course Syllabus
(SUBJECT TO CHANGE)
This semester will continue to be a challenge, and your instructors are committed to making this course as engaging as possible. We hope that you will take this seriously and work with us to make this as enjoyable as possible.
Course attendance is mandatory, although I will record classes in case there are absences as approved by the University. You are expected to participate in class through online polls and questions. These polls and questions will be noted and used at the discretion of the instructors when considering the final grade. There may also be pop quizzes that will be used in the same way, not as a formal part of your grade, just to make sure you are engaged. They will be more a barometer for you than a formal assessment on our part.
Thank you for you signing up for this class and please let us know what can be done to improve the class, not only at the end, but at any time and we will do our best to address any issues that may come up.
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Instructor: |
Dr. David Doermann |
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Office: |
113M Davis Hall |
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Email: |
Prefers to be contacted through Piazza |
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Office Hours: |
TBD |
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Zoom Link |
See Piazza - Resources - Staff |
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Teaching Asst: |
TBD |
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Email: |
Prefers to be contacted through Piazza |
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Office Hours: |
See Piazza - Resources - Staff |
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Zoom Link |
See Piazza - Resources - Staff |
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Teaching Asst: |
TBD |
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Email: |
Prefers to be contacted through Piazza |
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Office Hours: |
See Piazza - Resources - Staff |
|
Zoom Link |
See Piazza - Resources - Staff |
Lectures, Homeworks, Quizzes, Projects, and a final exam during the 14- week semester.
This course is an introduction to those areas of Artificial Intelligence that deal with fundamental issues and techniques of computer vision and image processing. The emphasis is on physical, mathematical, and information-processing aspects of vision. Topics to be covered include image formation, edge detection, and segmentation, convolution, image enhancement techniques, extraction of features such as color, texture, and shape, object detection, 3- D vision, and computer vision system architectures and applications.
The material is based on graduate-level texts augmented with research papers, as appropriate.
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History of CV/IP |
Local Descriptors |
Objects & Scenes |
Feature Selection/Boosting |
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Image Formation |
Alignment and Fitting |
Segmentation |
Evaluation |
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Image Processing |
Transforms |
Object Classification |
Vision Applications |
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Feature Extraction |
Homographies |
Object Recognition |
Datasets and Crowdsourcing |
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Filtering |
Ransac |
Stereo Image Formation |
Role of NNs |
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Edge Detection |
Texture |
Stereo Matching |
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Feature Detection and Matching |
Morphology
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Object Detection |
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Textbook: Computer Vision: Algorithms and Applications, by Richard Szeliski (online), published papers where applicable
- Class attendance and participation is expected
- You are responsible for ALL materials presented in class and assigned to read
- Quizzes will be given during class time only.
- There will be three projects
- Regular deliverables on the project will be graded during the course
- All assignments will be graded out of 100 points and weighted according to the table below
- I will drop ONE homework or Quiz Grade, whichever results in a higher overall grade
- All assignments will be turned in via UB Learns
- Quizzes and tests will be given online through the UB Learns system. For some quizzes and the final exam, you may be required to install the Respondus browser
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Weighting |
Assessment / Assignment |
Number |
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20% |
Homeworks |
4 |
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20% |
Quizzes |
4 |
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35% |
Projects |
3 |
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25% |
Final |
1 |
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100% |
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- It is entirely your responsibility to follow the policies outlined here and by the university
- Please ask the instructor(s) if you have questions.
Late Submission Policy
- Completed homework and project deliverables are to be submitted by their deadline (11:59pm).
- For homework, you will have up to 3 days to receive a grade reduced by 50%. No additional late days are allowed.
- Projects: You will be allowed a total of 3 days/partial day late submissions throughout the semester. Each late day beyond the three allowed will reduce your grade by 50%.
- No individual project/homework will be accepted after three days late.
Regrading Policy to Correct Grading Errors
- Assignments, quizzes, and exams may be submitted for regrading to correct grading errors.
- Regrade requests are due no later than one (1) week after the scores are posted.
- Regrade requests must be clearly written and attached to the assignment.
- When work is submitted for regrade, the entire work may be regraded, which may result in a lower grade.
- Work done in pencil may not be considered for regrading.
Exam Policy
- No makeup exams will be given except in provably extreme circumstances.
- Notify your instructor 24 hours prior to the exam via e-mail if you are going to miss it. If it is medically impossible for you to give prior notice, please obtain a note from a physician detailing the period (and the reason) you were medically incapable of communicating with the instructor.
- If you miss an exam/quiz because of sickness or similar reasons, visit a physician and obtain a note detailing the period and the reason you were medically incapable of taking the exam/quiz.
- You are responsible for knowing about the exam date. Please plan your travel and other activities accordingly.
Grading Policy
- No "I" (Incomplete) will be given except under provably extreme circumstances.
- There is no grade negotiation at the end of the semester.
Disabilities
- If you have a diagnosed disability (physical, learning, or psychological) that will make it difficult for you to carry out the course work as outlined or that requires accommodations such as recruiting note-takers, readers, or extended time on exams or assignments, please advise the instructor during the first two weeks of the course so that we may review possible arrangements for reasonable accommodations. In addition, if you have not yet done so, contact the Office of Disability Services.
All work must be your own
- Do not take the answers, words, ideas, or research findings of other people as yours; cite and acknowledge properly, and develop your own ideas.
- No cheating
- According to departmental policy, any violation of academic integrity will result in a Failing Grade for the course, and termination of departmental financial scholarship.
- Tools will be used to check similarities. Similar submissions will result in Failing Grade for all involved parties.
- Use of a code from an online repository (when permitted) must include proper and clearly visible attribution in your report.