21AI639:Computer Vision
(Even Semester 2022-2023)
M.Tech CSE/AI - II Sem
Instructor: Dr. Mansi Sharma and Dr. Raghesh Krishnan K
Lectures: Monday (Slot 4), Tuesday (Slot 3), Friday (Slot 3)
Lab Sessions: Tuesday (Slot 5-6)
Room No. - ABIII FF D206
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Course Description
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs. This course is designed to open the doors for students who are interested in learning about the fundamental principles and important applications of computer vision in applications such as security, healthcare, entertainment, mobility, augmented reality, etc. The course covers basic understanding of image formation and various image processing techniques. It also covers topics on modern computer vision architectures based on deep convolutional neural networks. Object detection, segmentation and object tracking will be introduced as part of the course. The course also gives exposure to projective geometry, camera calibration, stereo vision, camera projection models, 3D reconstruction and discuss their applications in SLAM (Simultaneous Localization and Mapping) applications.
Helpful Background
The basic knowledge of Linear Algebra, Calculus, Optimization, Image Processing, Machine Learning and/or Computer Graphics would be very useful.
Useful Online Resources
Related online courses useful for creating background for the study:
1. Multiple View Geometry - YouTube Lecture Series by Prof. Daniel Cremers (Technical University of Munich)
2. Computer Vision - YouTube Lecture Series by Prof. Andreas Geiger, University of Tübingen
3. Photogrammetry Lectures
Prof. Cyrill Stachniss (University of Bonn)
4. Fundamentals of Deep Learning - NPTEL Lectures by Prof. P. K. Biswas
Evaluation Pattern: 70:30
Midterm Exam - 20%
Lab Assignments – 25%
Evaluation Lab 1 (10 Marks)
Evaluation Lab 2 (15 Marks)
Project – 25%
End Semester Exam - 30%
Tentative Lab Schedule
Lab 1 Assignment (March 7th 2023)
Lab 2 Assignment (March 14th 2023)
Lab 3 Assignment (March 21st 2023) / Evaluation Lab 1
Lab 4 Assignment (March 28th 2023)
Lab 5 Assignment (April 11th 2023) / Evaluation Lab 2
Lab 6 Assignment (April 18th 2023)
Lab 7 Assignment (April 25th 2023)
Lab 8 Assignment (May 2nd 2023)
Lab 9 Assignment (May 9th 2023)
Lab 10 Assignment (May 16th 2023)
Lab 11 Assignment (May 23rd 2023)
Lab 12 Assignment (May 30th 2023)
Lab 13 Assignment (June 6th 2023)
Lab 14 Assignment (June 13th 2023)
Tentative Exam Schedule
Evaluation Lab 1 March 21st 2023
Evaluation Lab 2 April 11th 2023
Mid Term Exam Last week of April/First week of May
Project Review First/Second week of June
End Semester Exam Begins June 22nd 2023
Textbook/Reference Books
● Digital Image Processing by Rafael Gonzalez and Richard Woods, Pearson; 4th edition (10 May 2017)
● Computer Vision: A Modern Approach by Forsyth and Ponce, Pearson Education India; 2nd edition (1 January 2015)
● Computer Vision: Algorithms and Applications by Richard Szeliski, 2nd ed. 2022 Edition
● Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, Cambridge University Press;
2nd edition (April 19, 2004)
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