The information provided here can be treated as an unofficial syllabus. The official syllabus, compatible with the UTA template, will be posted on Canvas.
Description of Course Content: This course introduces students to basic concepts and techniques in computer vision. The topics covered include morphological operations, connected component analysis, image filters, edge detection, feature extraction, object detection, object recognition, tracking, gesture recognition, image formation and camera models, calibration, and stereo vision. A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics.
Prerequisites for CSE 4310: Admitted into an Engineering Professional Program. C or better in each of the following: CSE 3318 (Algorithms), IE 3301 (probabilities), and CSE 3380 or MATH 3330 (Linear Algebra).
Student Learning Outcomes: After successfully taking this course, a student should be familiar with basic techniques for addressing standard computer vision problems such as object detection, object recognition, tracking, calibration, and stereo vision. Students should be able to discuss pros and cons of these approaches, be able to implement these basic computer vision methods, and be able to apply such basic computer vision methods to real world problems.
The final semester score, calculated based on the percentages listed above, will be converted to letter grades based on the following scale:
Any request for re-grading must be made within 5 days of receipt of that grade. Re-grading can lead to a higher or lower grade, depending on grading errors that are discovered.
There will be little or no extra credit. If there are extra credit opportunities, they will be included as part of the assignments, and they will be available to all students. There will be no make-up opportunities, and there will be no way for individual students to do extra work and improve their grade at the end of the semester.
IMPORTANT: It should be clear to every student that course grades will depend EXCLUSIVELY on the above grading criteria. Students should not request nor expect any other factor to be considered in computing the course grade. For example, factors that will NOT be considered are: need of a better grade to keep financial aid, to stay in the program, to qualify for a job offer, or to graduate. Students are expected to carefully monitor their own performance throughout the semester and seek guidance from available sources (including the instructor) if they are concerned about their performance and the course grade that they will earn. However, if the assignment scores are not good enough to warrant the desired grade at the end of the semester, there will be no other recourse for improving the grade.
I pledge, on my honor, to uphold UT Arlington's tradition of academic integrity, a tradition that values hard work and honest effort in the pursuit of academic excellence. I promise that I will submit only work that I personally create or contribute to group collaborations, and I will appropriately reference any work from other sources. I will follow the highest standards of integrity and uphold the spirit of the Honor Code.
Instructors may employ the Honor Code as they see fit in their courses, including (but not limited to) having students acknowledge the honor code as part of an examination or requiring students to incorporate the honor code into any work submitted. Per UT System Regents' Rule 50101, paragraph 2.2, suspected violations of university's standards for academic integrity (including the Honor Code) will be referred to the Office of Student Conduct. Violators will be disciplined in accordance with University policy, which may result in the student's suspension or expulsion from the University.