CSE 327: Fundamentals of Computer
Vision
Fall
2024
Basic
Information:
· Lecture info: Mon/Wed 3:30pm – 4:50pm, Frey Hall 205
· Instructor: Haibin Ling (haibin.ling AT stonybrook.edu), NCS 147
· Office hour: Wed 9:30am-11:30am or by appointment
· TAs:
o Ming Lin, ming.lin@stonybrook.edu, office hour: Mon 9:30-11:30am
o Shivasankaran Vanaja Pandi, shivasankaran.vanajapandi@stonybrook.edu, office hour: Tue 9-11am
o Naman Joshi, namjoshi@cs.stonybrook.edu, office hour: Thu 12-2pm
o Rushil Nilesh Shah, rushshah@cs.stonybrook.edu, office hour: Mon 3-5pm
· Syllabus: PDF
Useful
Links:
· Main textbook: Computer Vision: Algorithms and Applications, Richard Szeliski, 2nd ed., 2022.
· Computer Vision: A Modern Approach, David Forsyth and Jean Ponce, 2nd ed., 2012.
· Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman, 2nd ed., 2004.
· Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
· Monocular Model-Based 3D Tracking of Rigid Objects: A Survey, Vincent Lepetit, Pascal Fua, 2005.
Course
Schedule (tentative, will be actively updated during semester)
|
Date |
Topics |
Materials |
|
Week 1 8/26 8/28 |
· Introduction o Topics, history, applications, related fields o Course logistics ·
Image Formation o Camera anatomy o Anatomy of cameras |
· Szeliski 1.1, 1.2 · Szeliski 2.1, 2.2, 2.2, 2.3 |
|
Week 2 9/4 |
·
Review of math and coding (by Kalyan Garigapati) o Basic math concept review o Tutorial to Google Colab |
|
|
Week 3 9/9 9/11 |
·
Image Formation o Photometric image formation ·
Image Processing o Point operations o Filters, convolution o Fourier transformation · Quiz 1 (9/11) |
· Szeliski 2.1, 2.2, 2.2, 2.3 · Szeliski 3.1-3.4 |
|
Week 4 9/16 9/18 |
·
Image Processing o Image smoothing o Histogram equalization o Image pyramid o Geometric transform o Image gradient o Points, corners, edges o Scale and orientation |
· Szeliski 3.4, 3.5, 3.6 · Szeliski 7.1, 7.2, 7.4, 7.4 |
|
Week 5 9/23 9/25 |
·
Model Fitting and Optimization o Line fitting, robust fitting o Hough transform o RANSAC |
· Szeliski 4.1, 4.2 · Szeliski 7.4, 8.1 |
|
9/29 |
·
Homework 1 Due
(11:59pm EST) |
|
|
Week 6 9/30 10/2 |
·
Machine Learning Concepts o Supervised learning o Unsupervised learning · Quiz 2 (10/2) |
|
|
Week 7 10/7 10/9 |
·
Elements of perspective geometry o Homogeneous coordinates o Camera geometry transformations o Epipolar geometry |
· Szeliski 5.5, 6.3 |
|
Week 8 10/16 |
·
Stereo o Stereo viewing |
|
|
10/20 |
Homework 2 Due (11:59pm EST) |
|
|
Week 9 10/21 10/23 |
·
Midterm (10/21) ·
Stereo o Stereo reconstruction |
· Szeliski 12.1, 12.2, 12.3, 12.4, 12.5, 12.6 ·
|
|
Week 10 10/28 11/30 |
·
Motion estimation and tracking o Translational alignment o Optical flow |
· Szeliski 9.1, 9.3 |
|
Week 11 11/4 11/6 |
·
Recognition o Instance recognition o Image classification
(traditional methods) ·
Deep Learning o Deep neural networks |
· Szeliski 5.3, 5.4, 5.5, 6.2 · Deep Learning, Goodfellow et al., 2016. · Deep Learning Tutorial, Stanford |
|
11/10 |
Homework 3 Due (11:59pm EST) |
|
|
Week 12 11/11 11/13 |
·
Deep Learning o Convolutional neural networks o Architecture ·
Deep Learning Practice o Training o Data augmentation |
· Szeliski 8.1, 8.2, 8.3 |
|
Week 13 11/18 11/20 |
·
Detection and segmentation o Traditional solution o Deep learning solutions o Semantic segmentation · Quiz 3 (11/18) |
· Szeliski 6.3, 6.4 · End-to-End Object Detection with Transformers, Carion, et al., 2020 · Segment Anything, Kirillov et al., 2023 |
|
11/24 |
Homework 4 Due (11:59pm EST) |
|
|
Week 14 11/25 |
·
Vision Transformer o Transformer o Transformer in computer vision |
· Szeliski 5.5 |
|
Week 15 12/2 12/4 |
·
Video analytics o Video object tracking o
· Quiz 4 (12/4) |
· Szeliski 9.4 |
|
Week 16 12/9 |
·
Advanced topic o Generative models · Final review |
|
|
12/10 |
Homework 5 Due (11:59pm EST) |
|
|
12/16 |
Final Exam, 5:30-8:00pm, Frey Hall 205 |
|