CSE 252A: Computer Vision I

Syllabus

Instructor: Ben Ochoa
Email: bochoa at ucsd.edu
Office hours: M 8:00 PM-9:00 PM (primary) and W 8:00 PM-9:00 PM (secondary), EBU3B 3234, and at other times by appointment

TA: Karan Santhosh
Email: ksanthosh at ucsd.edu
Office hours: Tu 1:00 PM-3:00 PM, EBU3B B270A, and at other times by appointment with instructor approval

TA: Rishikanth Chandrasekaran
Email: r3chandr at ucsd.edu
Office hours: W 6:30 PM-8:30 PM, EBU3B B250A, and at other times by appointment with instructor approval

TA: Mehmet Simsek
Email: msimsek at ucsd.edu
Office hours: Th 11:00 AM-1:00 PM, EBU3B B240A, and at other times by appointment with instructor approval

Note: when emailing the instructor or one of the TAs with questions about the class, please put "CSE 252A" in the subject line.

Class section ID: 250234
Lecture: MW 5:00 PM-6:20 PM, WLH 2005

This course provides a comprehensive introduction to computer vision providing broad coverage including low level vision (image formation, photometry, color, image feature detection), inferring 3D properties from images (shape-from-shading, stereo vision, motion interpretation) and object recognition.

Prerequisites: Linear algebra, calculus, probability and statistics. Python or other programming experience.

Assignments will be completed using a Jupyter Notebook with Markdown cells for text and math (images of handwritten text or math will not be accepted), and Code cells in 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. For students, this means that all academic work will be done by the individual to whom it is assigned, without unauthorized aid of any kind.

Collaboration Policy: It is expected that you complete your academic assignments on your own and in your own words and code. 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; 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 other than your own, 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.

Student Conduct Policy: Maintaining an academic community free from disruption/obstruction, physical abuse, harassment, and other conduct inconsistent with UCSD's Principles of Community is each of our responsibility and essential to promoting mutual respect and academic rigor. Non-academic student misconduct will be reported to the Center for Student Accountability, Growth, and Education for violating the University Standards of Conduct.

Grading: There will be 5 homework assignments and a final exam weighted with the following percentages:

Assignments: 60% (4% for assignment 0, 14% for each of the other 4 assignments)
Final exam: 40%

This course uses the standard grading scale:

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.

Assignments and exams:

• Assignment 0 and files (assigned Oct 4, due Oct 11)
• Assignment 1 and files (assigned Oct 11, due Oct 25)
• Assignment 2 and files (assigned Oct 25, due Nov 8)
• Assignment 3 and files (assigned Nov 8, due Nov 22)
• Assignment 4 and files (assigned Nov 22, due Dec 6)
• Final exam (Dec 14)

Handouts/Readings:

• Beyond Lambert: Reconstructing Specular Surfaces Using Color (Mallick et al.) [pdf]
• What Is the Set of Images of an Object under All Possible Illumination Conditions? (Belhumeur and Kriegman) [pdf]

Lecture slides:

• Lecture 1 Introduction and overview (Oct 2)
• Lecture 2 Geometric image formation (Oct 4)
• Lecture 3 Photometric image formation (Oct 9)
• Lecture 4 Photometric stereo (Oct 11)
• Lecture 5 Image filtering (Oct 16)
• Lecture 6 Edge detection and corner detection (Oct 18)
• Lecture 7 Calibrated stereo (Oct 23)
• Lecture 8 Calibrated stereo and feature matching (Oct 25)
• Lecture 9 Uncalibrated stereo and feature extraction (Oct 30)
• Lecture 10 Structure from motion (Nov 1)
• Lecture 11 Model fitting (Nov 6)
• Lecture 12 Optical flow and motion (Nov 8)
• Lecture 13 Recognition, detection, and classification (Nov 13)
• Lecture 14 Recognition, detection, and classification (Nov 15)
• Lecture 15 Neural networks (Nov 20)
• No lecture meeting (Nov 22)
• Lecture 16 Convolutional neural networks (Nov 27)
• Career day (Nov 29)
• Lecture 17 Color (Dec 4)
• Lecture 18 Human visual system (Dec 6)

Lecture topics (tentative):

• Introduction to computer vision
• Geometric image formation
• Photometric image formation
• Photometric stereo
• Image filtering
• Edge detection and corner detection
• Calibrated stereo
• Feature matching
• Uncalibrated stereo
• Feature extraction
• Structure from motion
• Robust model fitting
• Optical flow and motion
• Recognition, detection, and classification
• Neural networks
• Convolutional neural networks
• Color
• Human visual system

Optional, helpful textbooks:

Computer Vision: Algorithms and Applications, 2nd edition
Richard Szeliski
Springer, 2022
[Amazon]

Digital Image Processing, 4th edition
Rafael C. Gonzalez and Richard E. Woods
Pearson, 2018
[Amazon]

Introductory Techniques for 3-D Computer Vision
Emanuele Trucco and Alessandro Verri
Prentice Hall, 1998
[Amazon]
Multiple View Geometry in Computer Vision, 2nd edition
Richard Hartley and Andrew Zisserman
Cambridge University Press, 2004
[Cambridge Books Online] [Amazon] [Google]

Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville
MIT Press, 2016
[Amazon] [Google]

Diversity and Inclusion

We are committed to fostering a learning environment for this course that supports a diversity of thoughts, perspectives, and experiences while respecting your identities (including race, ethnicity, heritage, gender, sex, class, sexuality, religion, ability, age, educational background, etc.). Our goal is to create an inclusive learning environment where all students can feel comfortable and thrive. Accordingly, the instructional staff will make a concerted effort to be welcoming and inclusive to the wide range of students in this course. If there is some way we can help you feel more included, please let one of the course staff know (in person, via email/Piazza, or even using an anonymous note).

We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community. Please understand that others' backgrounds, perspectives, and experiences may be different than your own, and help us build an environment where everyone is welcomed and respected.

If you experience any sort of harassment or discrimination, please contact an instructor as soon as possible. If you prefer to speak with someone outside of the course, please contact the Office for the Prevention of Harassment and Discrimination.

Students with Disabilities

We aim to create an environment in which all students can succeed. If you have a disability, please contact the Office for Students with Disabilities (OSD) and discuss appropriate accommodations as soon as possible. We will work to provide you with the accommodations you need, but you must first provide a current Authorization for Accommodation (AFA) letter issued by the OSD. You are required to present your AFA letters to the instructor and to the department's OSD Liaison so that accommodations may be arranged.

Basic Needs/Food Insecurities

If you are experiencing any insecurities related to basic needs (food, housing, financial resources), there are resources available on campus to help, including The Hub and the Triton Food Pantry. Please visit The Hub for more information.