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General InformationTimes & Places
Lecture: TuTh 3:30PM - 4:50PM, CENTER 105, Zoom
|Instructor||Prof. Hao Sufirstname.lastname@example.org||2:00pm-3:00pm, Tue||CSE 4114 and course zoom|
|Course Assistant||Rishikanth Chandrasekaranemail@example.com||5:30pm-6:30pm, Tue||CSE B250A and course zoom|
|Course Assistant||Weijian Xufirstname.lastname@example.org||5:00pm-6:00pm, Wed||CSE B260A and course zoom|
|Course Assistant||Shilin Zhuemail@example.com||1:00pm-2:00pm, Wed||CSE B270A and course zoom|
- Camera Model
- Multi-View Geometry
- Structure from Motion
- Optical Flow
- Image Classification
- Basic Convolutional Neural Network
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 3D shape reconstruction through stereo, motion estimation, and image classification. 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 Python.
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 individual must write your own copy. 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: No late day is allowed. However, you can drop one out of nine assignments without penalty.
Homework, Exams, and Grading (tentative)
- Weekly Homeworks: 80%
- Final Project: 20%
- No In-class Exams