CS59300CVD: Comp. Vision With Deep Learning (Fall 2024)
Computer Vision
Course Information
Computer vision is a field that focuses on building machines that can see. In this course, we will cover the fundamentals of major tasks in computer vision, starting from the basics of image formation to modern computer vision methods based on deep learning. By the end of this course, students will have a solid foundation for conducting research in computer vision and the necessary technical background to understand and implement state-of-the-art vision papers.
Pre-requisites:
- CS 37300 Data Mining & Machine Learning
- MA 26500 Linear Algebra
- STAT 41600 Probability
Textbook:
- [FP] Computer Vision: A Modern Approach by David Forsyth and Jean Ponce (2nd ed.)
- [RS] Computer Vision: Algorithms and Applications by Richard Szeliski (2nd ed.)
- [DDL] Dive into deep learning by Zhang, Aston, et al.
Grading:
The final grade will be curved and no stricter than the cutoff: A+: 97-100, A: 93-96, A-: 90-92, B+: 87-89, ..., etc.The percentage is computed following (without any rounding):
- Assignments: 50% (12.5% each assignment)
- Midterm: 25%
- Final Project: 25%
FAQ:
- Lecture slides will be posted on Brightspace. Some materials are from other Professors as referenced in the slides; Do not redistribute.
- The instructor & TAs can be best reached through Ed Discussion. Please post your questions there instead of emailing TAs.
- During office hours or on Ed Discussion, please avoid posting partial homework solutions or asking TAs to "review" your code/solution.
- Tutorial for learning Latex with Overleaf: [Link]
Instructor & TAs
Raymond A. Yeh
Instructor
Email: rayyeh [at] purdue.edu
Office Hour: Monday 9AM
Location: Zoom
Chiao-An Yang
Teaching Assistant
Email: yang2300 [at] purdue.edu
Office Hour: Tuesday 9:30-10:30
Location: HAAS072
Time & Location
- Time: MWF (12:30PM - 01:20PM)
- Location: Max W & Maileen Brown Hall (BHEE 236)
Other Resource
Course Schedule
The following schedule is tentative and subject to change.
| Date | Event | Description | Readings |
|---|---|---|---|
| Aug 19 | Lecture 1 | Introduction & Overview
|
DDL 3 |
| Aug 21 | Lecture 2 | Applied Deep Learning - I
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DDL 3 |
| Aug 23 | Lecture 3 | Applied Deep Learning - II
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DDL 2 |
| Aug 26 | Info. | Assignment 1 Released
Select from the following: |
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| Aug 26 | Lecture 4 | Image Processing - I
|
RS 2 |
| Aug 28 | Lecture 5 | Image Processing - II
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| Aug 30 | Lecture 6 | Image Processing - III
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| Sept 2 | Info. | Labor Day
Select from the following: |
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| Sept 4 | Lecture 7 | Image filtering - I
|
FP 4 |
| Sept 6 | Lecture 8 | Image filtering - II (Cancelled, see recording on BrightSpace)
|
RS 3.4 |
| Sept 9 | Lecture 9 | Image filtering - III
|
DDL 7 |
| Sept 11 | Lecture 10 | Edge / Corner Detection - I
|
FP 5.1-5.2 |
| Sept 13 | Lecture 11 | Edge / Corner Detection - II
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FP 5.3 |
| Sept 15 | Deadline | Assignment 1 Due at 11:59PM
Select from the following: |
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| Sept 16 | Info. | Assignment 2 Released
Select from the following: |
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| Sept 16 | Lecture 12 | Edge / Corner Detection - III
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| Sept 18 | Lecture 13 | SIFT / Optical Flow- I
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| Sept 20 | Lecture 14 | SIFT / Optical Flow- II
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| Sept 23 | Lecture 15 | SIFT / Optical Flow- III
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| Sept 25 | Lecture 16 | Fitting & Alignment - I
|
FP 10.2-10.4, 22.1 |
| Sept 27 | Lecture 17 | Fitting & Alignment - II
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FP 12.1 |
| Sept 30 | Lecture 18 | Fitting & Alignment - III
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| Oct 2 | Lecture 19 | Fitting & Alignment - IV
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| Oct 4 | Lecture 20 | Cameras, Light, and Shading - I
|
FP 1 |
| Oct 6 | Deadline | Assignment 2 Due at 11:59PM
Select from the following: |
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| Oct 7 | Info. | Assignment 3 Released
Select from the following: |
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| Oct 7 | Info | Fall Break
Select from the following: |
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| Oct 9 | Lecture 21 | Cameras, Light, and Shading - II
|
FP 2 |
| Oct 11 | Lecture 22 | Cameras, Light, and Shading - III
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| Oct 13 | Deadline | Project Proposal Due at 11:59PM
Select from the following: |
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| Oct 14 | Lecture 23 | Midterm Review
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| Oct 16 | Deadline | Midterm
Select from the following: |
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| Oct 18 | Lecture 24 | Color
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| Oct 21 | Lecture 25 | Perspective projection - I
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FP 1 |
| Oct 23 | Lecture 26 | Perspective projection - II
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| Oct 25 | Lecture 27 | Camera calibration & Single-view modeling - I
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FP 1 |
| Oct 28 | Lecture 28 | Camera calibration & Single-view modeling - II
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| Oct 30 | Lecture 29 | Camera calibration & Single-view modeling - III
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| Nov 1 | Lecture 30 | Epipolar geometry & Structure from motion - I
|
FP 7.1 |
| Nov 3 | Deadline | Assignment 3 Due at 11:59PM
Select from the following: |
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| Nov 4 | Info. | Assignment 4 Released
Select from the following: |
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| Nov 4 | Lecture 31 | Epipolar geometry & Structure from motion - II
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FP 8 |
| Nov 6 | Lecture 32 | Epipolar geometry & Structure from motion - III
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| Nov 8 | Lecture 33 | Two-view stereo - I
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FP 7 |
| Nov 11 | Lecture 34 | Two-view stereo - II
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| Nov 13 | Info | No Lecture due to CVPR
Select from the following: |
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| Nov 15 | Lecture 35 | Multi-view stereo
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| Nov 18 | Lecture 36 | Light field modeling - I
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| Nov 20 | Lecture 37 | Light field modeling - II
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| Nov 22 | Lecture 38 | Image Classification, segmentation, detection
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DDL 4, 14 |
| Nov 24 | Deadline | Assignment 4 Due at 11:59PM
Select from the following: |
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| Nov 25 | Lecture 39 | Language and Vision
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| Nov 27 | Info | Thanksgiving
Select from the following: |
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| Nov 29 | Info | Thanksgiving
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| Dec 2 | Lecture 40 | Project Presentations
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| Dec 4 | Lecture 41 | Project Presentations
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| Dec 6 | Lecture 42 | Project Presentations
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| Dec 6 | Deadline | Final Project Report Due at 11:59PM
Select from the following: |