Modern Computer Vision

By Prof. A.N. Rajagopalan   |   IIT Madras
Learners enrolled: 155
This course explores both classical and deep learning-based approaches to computer vision. Starting from introduction to deep learning, it goes on to discuss traditional approaches as well as deep networks for a variety of vision tasks including low-level vision, 3D geometry, mid-level vision and high-level vision.

PREREQUISITES: Familiarity with image processing, linear algebra and probability is desirable but is not a must.

INDUSTRY SUPPORT: Google, Amazon, Facebook, Qualcomm, TI, KLA-Tencor, Siemens, GE, Philips etc
Course Status : Upcoming
Course Type : Elective
Duration : 12 weeks
Start Date : 23 Jan 2023
End Date : 14 Apr 2023
Exam Date : 29 Apr 2023 IST
Enrollment Ends : 30 Jan 2023
Category :
  • Electrical, Electronics and Communications Engineering
Credit Points : 3
Level : Undergraduate/Postgraduate

Page Visits

Course layout

Week 1: Course introduction, Introduction to deep learning, Introduction to neuron
Week 2: Multilayer perceptron (MLP), Gradient descent, Backpropagation in MLP
Week 3: Optimization and regularization, Regularization and preprocessing, Convolutional neural network (CNN)
Week 4: CNN properties, CNN architectures, Introduction to recurrent neural network (RNN), Encoder-Decoder models in RNN
Week 5: Low-level vision, Spatial and frequency domain filtering, Edge detection
Week 6: Line detection, Feature detectors, Harris corner detector
Week 7: Blob detection, SIFT, Feature descriptors, SURF
Week 8: Single-view geometry, 2D Geometric transformations, Camera intrinsics and extrinsics
Week 9: Two-view stereo, Algebraic representation of epipolar geometry, Fundamental matrix computation
Week 10: Structure from motion, Batch processing in SFM, Dense 3D reconstruction
Week 11: Deepnets for stereo and SFM, Mid-level vision, Image segmentation
Week 12: Deepnets for segmentation, High-level vision, Deepnets for object detection

Books and references

R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010 (online draft)

Instructor bio

Prof. A.N. Rajagopalan

IIT Madras
Dr. A.N. Rajagopalan is a Professor of Electrical Engineering at IIT Madras and specializes in the areas of Image Processing and Computer Vision. He is a Fellow of national and international academies, and Editorial Board member of flagship journals of IEEE in the above areas. He has co-authored two books.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 29 April 2023 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras. It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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