- Welcome to the course!
- Please sign up with Piazza
- Important We will have a 45-minute closed book exam in the first class for mathematical background evaluation, covering elementary knowledge of probability, linear algebra, and calculus. This test will be counted as 5 extra credits in your final score.
General InformationTimes & Places
Lecture: TuTh 3:30PM - 4:50PM, CENTER 113
Discussion: Fr 8:00AM - 8:50AM, CENTER 216
|Instructor||Prof. Hao Sufirstname.lastname@example.org||TBD||CSE Building 4114|
|Course Assistant||Tongzhou Muemail@example.com||TBD||CSE Building 4127|
|Course Assistant||Meng Songfirstname.lastname@example.org||TBD||CSE Building 4127|
|Course Assistant||Owen Michael Jowemail@example.com||TBD||CSE Building 4127|
SyllabusPlease find here.
The goal of computer vision is to compute properties of the three-dimensional world from images and video. Problems in this field include identifying the 3D shape of a scene, determining how things are moving, and recognizing familiar people and objects. This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, object recognition, and 3D shape reconstruction through stereo, photometric stereo, and structure from motion. To reflect the latest progress of computer vision, we also include a brief introduction to the philosophy and basic techniques of deep learning methods.
Prerequisites: Linear algebra and calculus; data structures/algorithms; and Python or other programming experience.
Programming aspects of the assignments will be completed using MATLAB
Academic Integrity Policy: Integrity of scholarship is essential for an academic community. The University expects that both faculty and students will honor this principle and in so doing protect the validity of University intellectual work. In this class, we encourage students to form groups of two and work together on homeworks. This means that all academic work will be done by the pair of individuals to whom it is assigned, without unauthorized aid of any kind.
Collaboration Policy: It is expected that you complete your academic assignments in your own words (more specifically, for any write-up assignment each individual must submit an independent copy). For coding tasks, each group can submit one copy together. The assignments have been developed by the instructor to facilitate your learning and to provide a method for fairly evaluating your knowledge and abilities (not the knowledge and abilities of others). So, to facilitate learning, you are authorized to discuss assignments with others (even if he/she is not your team member); however, to ensure fair evaluations, you are not authorized to use the answers developed by another, copy the work completed by others in the past or present, or write your academic assignments in collaboration with another person.
If the work you submit is determined to be violating the rules, you will be reported to the Academic Integrity Office for violating UCSD's Policy on Integrity of Scholarship. In accordance with the CSE department academic integrity guidelines, students found committing an academic integrity violation will receive an F in the course.
Late Policy: Assignments will have a submission procedure described with the assignment. Assignments submitted late will receive a 15% grade reduction for each 12 hours late (i.e., 30% per day). Assignments will not be accepted 72 hours after the due date. If you require an extension (for personal reasons only) to a due date, you must request one as far in advance as possible. Extensions requested close to or after the due date will only be granted for clear emergencies or clearly unforeseeable circumstances. You are advised to begin working on assignments as soon as they are assigned.
- Homeworks (4 assignments) 40%
- Mid-term 20%
- Final 40%