August-December 2017
CSL 462 - Computer Vision (UG)
CSL 618 - Computer Vision (PG)
Lectures (NIELIT Campus)
Wednesday – H8 – 2:55-3:45PM
Thursday – H8 – 3:50-4:40PM
Friday – H8 – 4:45-5:35PM
Labs
Thursday 09:00-10:45 AM - Lab 2
Friday 10:50-12:35 AM - Lab 3
Teaching Assistants
Amanjot Kaur (amanjot.kaur at iitrpr)
Shreya Ghosh (shreya.ghosh at iitrpr)
Showcase Assignment Submissions
Assignment 1 - Naman Goyal Sujit Rai Devendra Pratap Yadav
Assignment 2 - Shivam Mittal Prashant Patil Jaspinder Kaur
Assignment 3 - Akshay Dudhane Mayank Kumar Rajat Sharma
Slides (Can be accessed using IITRPR account only)
Mid-term topics survey (29/09/2017) [LINK]
End-term summary (27/11/2017) [LINK]
Reading material above point to Richard Szeliski's book until unless explicitly specified.
Marks distribution
Note the following can change on the instructor's desecration.
CSL 462
Assignments – 20%
Mid-term – 20%
End-term – 20%
Project – 40%
CSL 618
Assignments – 30%
Mid-term – 10%
End-term – 20%
Project – 40%
5% extra marks for research paper quality work in the project/assignment!
CSL 462 and 618 will have 2 different assignments
Assignment code in Matlab
Project can be in Matlab, C/C++ or Android/iOS
Minimum pass marks – 40/100
Minimum attendance – 70% (institute requirement)
Plagiarism
First instance of assignment plagiarism will lead to marks deduction.
Second instance of assignment plagiarism will lead to F.
Project plagiarism will be awarded F.
An interesting and quick read on how not to plagiarize - http://advice.writing.utoronto.ca/using-sources/how-not-to-plagiarize/
Assignment format – All reports have to be created in Latex by following the BMVC extended abstract.
Project can be both individual and group (2/3 max). List of projects will be shared (22/08/2017).
You can propose a new idea!
Project proposal to be submitted by 29/08/2017
The marking criteria will be shared with the project ideas.
Books
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
Available free online: szeliski.org/book
Reference book:
Computer Vision: Modern Approach, Jean and Ponce
Other resources
Linear algebra refresher - https://stanford.edu/~arbenson/refresher/la-refresher-slides.pdf
Topic to be covered
Note the following can change on the instructor's desecration.
Introduction to Computer Vision, history and current
Image filtering and matching
Edge detection and pyramids
Keypoint detection and scale invariant keypoints
Local descriptors, robust matching, homographies
Camera models and stereo
Machine learning from computer vision perspective: introduction
Image recognition and Bag of words
Face detection and analytics
Pose detection and attributes
Computer Vision and affect
Deep learning in computer vision
Applications
Research papers
Acknowledgement - "Stand on the shoulders of the giants"
This course is based on materials from Prof. Mubarak Shah, Prof. James Hays, Prof. Roland Goecke, Prof. Steve Seitz, Prof. Kristen Grauman and Prof. Sanja Fidler's amazing computer vision courses.