CSE 327: Fundamentals of Computer Vision

(Undergraduate Course)

Spring 2022

Basic Information:

Lecture info: Tu 5:45pm - 8:40pm, LGT ENGR Lab 152 or online
Instructor: Prof. Haibin Ling, haibin.ling AT stonybrook.edu
Office hour: Tue 2pm-4pm or by appointment
TAs: TBA
- Sounak Mondal (Sounak.Mondal@stonybrook.edu)
- Shail Sanjaykumar Raval (sraval@cs.stonybrook.edu)
- Yash Fulwani (yfulwani@cs.stonybrook.edu)
Syllabus: PDF

Special Note Spring 2022: Students are expected to attend every class, report for examinations and submit major graded coursework as scheduled. If a student is unable to attend lecture(s), report for any exams or complete major graded coursework as scheduled due to extenuating circumstances, the student must contact the instructor as soon as possible. Students may be requested to provide documentation to support their absence and/or may be referred to the Student Support Team for assistance. Students will be provided reasonable accommodations for missed exams, assignments or projects due to significant illness, tragedy or other personal emergencies. In the instance of missed lectures or labs, the student is responsible for reviewing posted slides, reviewing recorded lectures, seeking notes from a classmate or identified class note taker, writing lab reports based on sample data. Please note, all students must follow Stony Brook, local, state and Centers for Disease Control and Prevention (CDC) guidelines to reduce the risk of transmission of COVID.

Useful Links:

Computer Vision: Algorithms and Applications, Richard Szeliski, 2nd ed., 2021.
Computer Vision: Models, Learning, and Inference, Simon J.D. Prince, 2012.
Pattern Recognition and Machine Learning, Christopher M. Bishop.

Date	Lecture Topics (tentative schedule)	Materials (for references)
Week 1 1/25	General Introuction Background, topics, applications, related fields Course logistics Image formation basics Baisc facts about light Camera anatomy	Szeliski 1.1, 1.2 Szeliski 2.1, 2.2, 2.3
Week 2 2/1	Filtering and edges Point operations Filters, convolution, Fourier transforms	Szeliski 3.1, 3.2, 3.3
Week 3 2/8	Image features Smoothing images Image pyramid Image gradient Points, corners, edges Scale and orientation	Szeliski 3.3, 3.4, 3.5 Szeliski 7.1, 7.2
Week 4 2/15	Model Fitting Lines and curves Hough transform Alingment Quiz 1	Szeliski 4.1, 7.4, 8.1
2/20	Homework 1 due (11:59pm EST)
Week 5 2/22	Elements of perspective geometry Homogeneous coordinates Camera geometry transformations Epipolar geometry	Szeliski 2.1, 8.1, 8.2
Week 6 3/1	Machine Learning Concepts Dimensionality reduction Clutering Regression and classification
Week 7 3/8	Recognition basics / Bag of features Object representation Classifiers and category recognition Miterm review Quiz 2
3/13	Homework 2 due (11:59pm EST)
	Spring Break
Week 8 3/22	Midterm
Week 9 3/29	Deep Learning Introduction Deep neural networks Convolutional neural networks Architecture Applications	Szeliski 5.3, 5.4, 5.5 Deep Learning, Goodfellow et al., 2016. Deep Learning Tutorial, Stanford.
Week 10 4/5	Convolutional Neural Networks Training Data augmentation Deep Learning Practice Pretraining Data augmentation
4/10	Homework 3 due (11:59pm EST)
Week 11 4/12	Detection and segmentation Traditional solutions Deep learning solutions Semantic segmentation Quiz 3	Szeliski 6.3, 6.4
Week 12 4/19	Stereo Stereo viewing Stereo reconstruction	Szeliski 12.1, 12.2, 12.3, 12.5
4/24	Homework 4 due (11:59pm EST)
Week 13 4/26	Motion and Tracking Low level motion and optical flow Recurrent neural networks Object tracking	Szeliski 9.3, 9.4
Week 14 5/3	Advanced Topics Spatial augmented reality Transformer Quiz 4	Szeliski 11
5/9	Homework 5 due (11:59pm EST)
5/16	Final Exam, Monday 5/16, 5:30pm-8:00pm EST, in-person at LTENGR 152