Instructor: Manmohan Chandraker

Email: mkchandraker [AT] eng [DOT] ucsd [DOT] edu

Lectures: WF 6:30-7:50pm on Zoom

Instructor office hours: Thu 2-3pm on Zoom

TA: Rui Zhu (rzhu@eng.ucsd.edu)

TA office hours: Tue 1-2pm on Zoom

Discussion section: Mon 5-5:50pm on Zoom

Class discussion and message board: Piazza

Computer vision has made tremendous progress in recent years and is increasingly becoming a part of our daily lives. It seeks to analyze images to draw meaningful conclusions, which often requires drawing upon prior knowledge. This makes machine learning a useful tool, where the advent of deep neural networks has led to significant gains over the past five years. There is widespread acceptance that computer vision will play a major role in enabling technologies of the future, such as self-driving cars or augmented reality. The goal of the class is to equip students with the knowledge and skills to pursue higher studies or industry careers in modern computer vision.

A background in linear algebra and calculus is required. Programming experience in Python is required. Courses that cover these might be Math 20F, CSE 100 or Math 176, CSE 101 or Math 188. Prior knowledge of basics in computer vision is recommended, as covered by CSE 152. Students are encouraged to contact the instructor if unsure about meeting any criteria for enrollment.

The course will primarily involve lectures by the instructor. The goal of the course is to develop an understanding of the current state of computer vision and gain appreciation of its limits and potential. Students are encouraged to actively ask questions that the instructor may discuss.

Grades will be weighted as 40% for a final exam, 25% for a mid-term and 35% for assignments. There will be three homework assignments and a few ungraded quizzes.

- Feature detection and matching
- Structure from motion
- Face recognition
- Human pose estimation
- Object detection
- Semantic segmentation
- Deep Neural Networks
- Support Vector Machines
- Boosting
- Domain adaptation

- Lecture slides: [PDF]

- Solutions [PDF]

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- Lecture slides: [PDF]
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- Instructions: [PDF]
- Solutions: [Jupyter Notebook]

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- Lecture slides: [PDF]
- References:
- Five Point Relative Pose
- Three Point Absolute Pose (can ignore algebraic details)
- ORB-SLAM

- Solutions [PDF]

- Lecture slides: [PDF]
- References:

- Instructions: [PDF]
- Solutions: [Jupyter Notebook]

- Lecture slides: [PDF]
- References:

- Solutions [PDF]

- Lecture slides: [PDF]
- References:

- Instructions: [PDF]

- Lecture slides: [PDF]

- Lecture slides: [PDF]

- Lecture slides: [PDF]

- Solutions [PDF]

- Lecture slides: [PDF]

- Exam question set: [PDF]

- This course does not require a textbook. Some lectures will derive material from freely available online texts, such as:
- We might refer to some papers, which will be provided as PDFs or made available for download through provided links.

Manmohan Chandraker Last modified: Fri, Mar 08, 2019