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Many of the slides are modified from
the excellent class notes of similar courses offered in other schools by Prof
James Hays,
Noah Savely,
Jianbo Shi,
Marshall Tappen,
Fredo Durand,
Alexei Efros,
William Freeman,
Svetlana Lazebnik,
Srinivasa Narasimhan,
Richard Szeliski, and Li Zhang.
The instructor is extremely thankful to the researchers for making their notes
available online. Please feel free to use and modify any of the slides, but
acknowledge the original sources where appropriate.
Tentative Syllabus
|
Lecture Date |
Topic |
Material |
Homeworks |
| 1 |
1/12 |
M |
What is Computer Vision?
|
ppt, pdf
Readings: Szeliski, Ch. 1
|
|
| 2 |
1/15 |
Th |
Linear Filter
|
ppt, pdf,DemoCode
Readings: Szeliski, Ch.3.1 and 3.2
|
|
| 3 |
1/22 |
Th |
Gaussian Filter, Scipy tutorial
|
ppt, pdf,Filtering.py
Readings: Szeliski, Ch. 3.2, Solem, Ch.3.
|
Homework 1 is out
|
| 4 |
1/26 |
M |
Border Effect, Image Derivatives
|
ppt, pdf
Readings: Szeliski, Ch.3.3
|
|
| 5 |
1/29 |
Th |
Linear Algebra
|
ppt, pdf
Readings: geometric view of linear algebra
|
|
| 6 |
2/2 |
M |
Image derivatives, Median filter, Image denoising
|
ppt, pdf
Readings: Szeliski, Ch 3.3.1 Median Filter; Solem, Ch 1
|
Homework 1 is due
|
| 7 |
2/5 |
Th |
Thinking in Frequency, Fourier Transforms
|
ppt, pdf,
Demo code on FFT
Readings: Szeliski, Ch 3.4
|
|
8 |
2/9 |
M |
Filtering in Fourier Space, Python Exercises
|
ppt, pdf
ImageFilering.py
FFTAnalysis.py
Readings: Discrete FFT
|
Homework 2 is out
|
| Guest Lecture |
2/12 |
Th |
Guest Lecture: Katerina Fragkiadaki , Video Segmentation and Multi-Object tracking in the Era of Deep Learning
|
ppt, pdf,
|
|
| 9 |
2/16 |
M |
Image Sampling, Aliasing
|
ppt, pdf
Readings: Aliasing
|
|
| 10 |
2/19 |
Th |
Image Pyramids
|
ppt, pdf,
|
|
| 11 |
2/23 |
M |
Image Blending
|
ppt, pdf
Readings: Chapter 3.5
|
Homework 3 is out
|
| 12 |
2/26 |
Th |
Edge Detection
|
ppt, pdf,
|
|
| 13 |
3/2 |
M |
The Dress and color constancy
|
ppt, pdf
Readings: Chapter 2.3
|
|
| 14 |
3/5 |
Th |
Boundary Detection
|
ppt, pdf,
|
|
| No Class |
3/6-3/15 |
M/Th |
Spring Break No Classes
|
|
Homework 3 is due
|
| 15 |
3/16 |
M |
Feature Detection, Harris Corner
|
ppt, pdf
Readings: Chapter 4.1.1, Solem Chapter 2
|
Mid-term Take-home due
|
| 16 |
3/23 |
Th |
Harris Corner, Blob Detection, Mid-term review
|
ppt, pdf
Readings: Chapter 4.1.1, Solem Chapter 2
|
|
| 17 |
3/26 |
M |
Scale Invariance, Midterm
|
ppt, pdf
Readings: Chapter 4.1, Solem Chapter 2
|
|
| 18 |
3/30 |
Th |
Feature Discription, SIFT
|
ppt, pdf
Readings: Chapter 4.1.1, Solem Chapter 2
|
|
| 19 |
4/2 |
M |
feature matching
|
ppt, pdf
Readings: Chapter 4.1.2, Solem Chapter 2
|
|
| 20 |
4/6 |
Th |
Geometric Transformation, Affine transformation
|
ppt, pdf
Readings: Chapter 2.1, Solem Chapter 3
|
|
| 21 |
4/9 |
Th |
Homographies, Projective geometry
|
ppt, pdf
Readings: Chapter 3.6, Solem Chapter 3
|
Homework 4 is out
|
| 22 |
4/13 |
M |
Image Alignment and RASANC
|
ppt, pdf
Readings: 4.1, 6.1
Readings:Review of System of linear equations
Readings:Image Alignment
|
|
| 23 |
4/16 |
Th |
Camera Models
|
ppt, pdf
Readings:
|
|
| 24 |
4/20 |
M |
Introduction to machine learning
|
ppt, pdf
Readings:ppt">Intro to Machine learning
|
|
| 25 |
4/27 |
M |
Introduction to Recognition
|
ppt, pdf
Readings:
|
Homework 4 is due
|
| 26 |
4/30 |
Thur |
Final Exam Week No class
|
|
|
| 27 |
6/4 |
M |
Final Projects Due
|
|
|
|