CSE 527: Introduction to Computer
Vision
(Graduate
Course)
Fall
2024
Basic
Information:
· Lecture info: Mon/Wed 6:30pm – 7:50pm, LGT ENGR LAB 102
· Instructor: Haibin Ling (haibin.ling AT stonybrook.edu), NCS 147
· Office hour: Wed 9am-11am or by appointment
· TAs:
o Md Shahrar Fatemi (mfatemi AT cs.stonybrook.edu), office hour: Thu 4-6pm
o Snehal Tomar (stomar AT cs.stonybrook.edu), office hour: Fri 12-2pm
o Yufeng Wang (yufengwang AT cs.stonybrook.edu), office hour: Thu 3:30-5:30pm
· 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 Geometric primitives and transformations |
· Szeliski 1.1, 1.2 · Szeliski 2.1, 2.2, 2.2, 2.3 |
Week 2 9/4 |
·
Review of math and coding (by Yufeng Wang) o Basic math concept review o Tutorial to Google Colab |
|
Week 3 9/9 9/11 |
·
Image Formation o Photometric image formation ·
Guest lecture (9/11) by Prof. Chenyu You o Robust Machine Learning for Biomedical Data: Efficiency, Reliability, and Generalizability |
· Szeliski 2.2, 2.3 ·
|
Week 4 9/16 9/18 |
·
Image Processing o Linear filtering: separable filtering o Non-linear filtering, bilateral filtering o Fourier transforms o Image pyramid o Geometric transform ·
Model Fitting and Optimization o Line fitting, robust fitting o Hough transform o RANSAC |
· Szeliski 3.2, 3.3, 3.4, 3.5, 3.6 · Szeliski 4.1 · Szeliski 7.4, 8.1 |
9/22 |
·
Homework 1 Due
(11:59pm EST) |
|
Week 5 9/23 9/25 |
·
Machine Learning Overview o Supervised learning o Unsupervised learning ·
Recognition o Instance recognition o Image classification (traditional methods) |
· Szeliski 5.1, 5.2 · Szeliski 6.1, 6.2 |
Week 6 9/30 10/2 |
·
Deep Learning o Deep neural networks o Convolutional neural networks o Architecture o Applications |
· Szeliski 5.3, 5.4, 5.5, 6.2 · Deep Learning, Goodfellow et al., 2016. · Deep Learning Tutorial, Stanford |
Week 7 10/7 10/9 |
·
Deep Learning Practice (by Tao Sun) o Training o Data
augmentation ·
Transformer & Vision Transformer (by
Tao Sun) o Transformer o Vision Transformer |
· Szeliski 5.5, 6.3 |
Week 8 10/16 |
·
Detection o Traditional solutions o Deep learning solutions |
|
10/20 |
Homework 2 Due (11:59pm EST) |
|
Week 9 10/21 10/23 |
·
Midterm (10/21) ·
Detection and Segmentation o Detection with Transformers o Segmentation |
· Szeliski 6.3, 6.4 · End-to-End Object Detection with Transformers, Carion, et al., ECCV 2020 |
Week 10 10/28 10/30 |
·
Semantic Segmentation o Segmentation - conventional o Semantic segmentation – deep learning solutions |
· Szeliski 6.3, 6.4 · Segment Anything, Kirillov et al., 2023 |
Week 11 11/4 11/6 |
·
Feature detection and matching o Points
and patches o Edges
and contours ·
Motion estimation and tracking o Review of alignment o Optical flow |
· Szeliski 7.1, 7.2, 7.5 |
11/10 |
Homework 3 Due (11:59pm EST) |
|
Week 12 11/11 11/13 |
·
Depth estimation o Epipolar
geometry ·
Guest lecture (11/13) o 6D pose estimation by Ruyi Lian |
· Szeliski 8.1, 8.2, 8.3 · Szeliski 9.1, 9.3, 9.4 |
Week 13 11/18 11/20 |
·
Depth estimation o Correspondences o Local and global methods o Deep
learning solution ·
Motion estimation and tracking o Object
tracking |
· Szeliski 9.1, 9.3, 9.4 |
Week 14 11/25 |
·
Structure from motion and SLAM o Two-frame structure from motion o Multi-frame structure from motion o Simultaneous localization & mapping (SLAM) |
· Szeliski 12.1, 12.2, 12.3, 12.4, 12.5, 12.6 |
12/3 |
Homework 4 Due (11:59pm EST) |
|
Week 15 12/2 12/4 |
·
Generative models o Autoencoder o Generative adversarial network o Diffusion
models · Final review (12/2) |
· Szeliski 11.1, 11.2, 11.3, 11.4, 11.5 |
Week 16 |
|
|
12/9 |
Final Exam, 6:30-9:00pm LGT ENGR LAB 102 |
|