- Instructor: Arjun Jain
- Office: 216, CSE New Building
- Email: ajain@cse DOT iitb DOT ac DOT in
- Teaching Assistants: Rishabh Dabral, Safeer Afaque
- Instructor Office Hours (in room 216 CSE New Building): Arjun is on campus only on Thursdays and Fridays. Meet him after class or fix an appointment over email.
- Camera geometry, camera calibration, vanishing points, important transformations, homographies
- Image registration: RANSAC for point-matching, SIFT overview
- Deep Learning in computer vision: the data-driven paradigm, feed forwards networks, back-propagation and chain rule; CNNs and their building blocks, generative adverserial networks (GANs)
- Deep Learning applications including face detection, CNN compression, siamese and triplet networks and applications to face recognition
- Algorithms for: shape from shading, optical flow, Kanade-Lucas-Tomasi algorithm, applications of optical flow
- Photometric stereo - deriving shape from multiple images of an object taken under different lighting conditions; applications to illumination invariant face recognition, face relighting
- Stereo (geometric binocular): epipolar geometry and fundamental matrix, the correspondence problem and shape from stereo; structure from motion
- Lecture slides that will be regularly posted
- Computer Vision: Algorithms and Applications, by Richard Szeliski
- Fundamentals of Computer Vision, by Mubarak Shah
- Deep Learning, by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- All iTorch notebooks for topics covered in class can be found here
- Mid-sem exam: 20%
- Final exam (cumulative): 20%
- Assignments (five or six): 35% (all to be done in groups of 2-3 students)
- Course project: 20% (to be done in the same group of 2-3 students)
- Class participation: 5%
- Course project work will be presented by the student group during a viva at the end of the course. During this viva, each student in the group will be separately questioned, not only on the project work, but also the assignments. Each student is expected to contribute to each and every assignment and the course project.
- Audit requirements: You must write both exams, submit all assignments and the project, and score at least 40% to get an AU.
- Assignments will be given out (typically) once every two or three weeks. They must be submitted on or before the deadline. No late assignments will be accepted. The programming components of the assignments will typically involve MATLAB and lua, so you must be willing to learn it quickly.
- We will adopt a zero-tolerance policy against any forms of plagiarism or any other form of cheating. Just don't do it! In cases of plagiarism, givers and takers will both be considered equally responsible.
- This course is (inherently) cumulative. The syllabus for the final exam will include everything taught during the semester.
[02/02/2018] Course projects have now been finalized.
Go to this link for the finalized list.
- [12-Jan-18] Assignment 1 has been released. The due date for submission is Friday, January 26, 2018.
- [27-Jan-18] Assignment 2 has been released. The due date for submission is Sunday, February 4, 2018.
- [09-Feb-18] Assignment 3 has been released. The due date for submission is Wednesday, February 21, 2018. Corresponding kaggle competition link
- [06-Mar-18] Assignment 4 has been released. The due date for submission is Monday, March 19, 2018. Corresponding kaggle competition link
- [24-March-18] Assignment 5 on Tracking has been released. Due date: April 2, 2018. Download the necessary files from here
- [11-April-18] Assignment 6 on Multiview Geometry has been released. Due date: April 19, 2018.
Date | Topics | Slides | iTorch Notebooks | Extra Reading |
---|---|---|---|---|
4th Jan. 2018 |
|
Slides | -- | -- |
5th Jan. 2018 |
Camera Geometry
|
Slides | -- | Homogeneous Representations of Points, Lines and Planes |
12th Jan. 2018 |
|
Slides | -- | -- |
13th Jan. 2018 |
|
Slides | -- | Resource on SVD, how/why it can be used to solve eq. sytems of type Ax=0, |x|=1 |
18th Jan. 2018 |
|
Slides(1) Slides(2) |
-- | -- |
19th Jan. 2018 |
|
Slides(1) Slides(2) |
-- | -- |
25th Jan. 2018 |
Recognizing images, objects, scenes (Prof. Suyash P. Awate)
|
Slides |
-- | -- |
1st Feb. 2018 |
Recognizing images, objects, scenes (Prof. Suyash P. Awate)
|
Slides |
-- | -- |
2nd Feb. 2018 |
Robust Methods in Computer Vision
|
Slides(1) Slides(2) |
KNN | Matrix calculus reminder |
8th Feb. 2018 |
|
Slides |
Gradient Check | ADAM,
Nesterov DL optimization algorithms overview |
9th Feb. 2018 |
|
Slides |
Linear Layer, ReLU | -- |
15th Feb. 2018 |
|
Slides |
MaxPool, Convolution, Transposed convolution, Dropout | Convolution arithmetic for deep learning |
16th Feb. 2018 |
|
Slides |
Cross Entropy, Weight Initialization | -- |
22nd Feb. 2018 |
|
Slides |
-- | -- |
23rd Feb. 2018 |
|
Slides |
-- | -- |
8th March 2018 |
|
Slides |
-- | -- |
9th March 2018 |
|
Slides Slides |
MNIST Vanilla GAN | -- |
15th March 2018 |
|
Slides | -- | -- |
16th March 2018 |
Structure from Motion
|
Slides |
-- | -- |
22nd March 2018 |
Kanade-Lucas-Tomasi Feature Tracking (KLT)
|
Slides |
-- | Lucas-Kanade 20 Years On: A Unifying Framework |
23rd March 2018 |
Geometric Stereo
|
Slides |
-- | -- |
5th April 2018 |
|
Slides |
-- | -- |
6th April 2018 |
|
Slides(1) Slides(2) |
-- | -- |
12th April 2018 |
|
Slides |
-- | -- |
19th April 2018 |
|
Slides |
-- | -- |
20th April 2018 |
|
Slides |
-- | -- |