AID-565, Computer Vision
Spring 2021-22
Instructor: Balasubramanian Raman
Other Instructors: Sanjeev Kumar and Sparsh Mittal
Office: S-227, CSE Building
Class Meeting Time: Mondays, Tuesdays (10:00-10:55 am), Thursdays (9:00-9:55 a.m).
Class Room: Microsoft Teams
Office Hours: Wednesdays, Fridays 11:00 a.m. - 1:00 p.m. and by appointment
TAs: Gaurav Bhardwaj, Puneet Kumar and Phoolpreet
Email: first four letters of first name at cs dot ac dot in
Announcements
April 24, 2022: End Term Examinations.
March 17, 2022: Assignment 3 has been uploaded.
March 10, 2022: Mid-Term Marks have been uploaded.
March 02, 2022: Mid-Term Examinations.
February 17, 2022: Assignment 2 has been uploaded.
February 10, 2022: Assignment 1 has been uploaded.
February 03, 2022: Second session of classes have begun (13 lectures by R. Balasubramanian).
Course Objectives, Learning Outcomes and Prerequisites
To provide a glimpse of what computer vision is about
To give an understanding of image processing for computer vision
To know about Image formation and camera calibration
To impart the knowledge on Feature detection and matching
Course Outcomes:
After completing the course you will be able to:
identify basic concepts, terminology, theories, models and methods in the field of computer vision,
describe known principles of human visual system,
describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition,
suggest a design of a computer vision system for a specific problem
Prerequisites: NIL.
Evaluation Components
- Term tests/quizzes (10%)
- Assignments + Practical (10%)
- Class participation (5%)
- Mid-Term Examination (20-30%)
- End Term Examination (40-50%)
Lecture Notes
01. Introduction to Computer Vision (03/02/2022)
02. Field of View and Stereo Vision (07/02/2022)
03. Reconstruction of 3D primitives from Stereo and sequence of Images (08/02/2022)
04. 3D Plane and a Straight Line (10/02/2022)
05. 2D Transformations and Viewing in 3D (15/02/2022)
06. One Point, Two point and Three point Perspective Projections, Parallel Projections and Geometric Camera Parameters (21/02/2022)
07. Extrinsic and Intrinsic camera parameters, Perspective and Weak Perspective Camera models (22/02/2022)
08. Epipolar Geometry and camera parameters (24/02/2022)
09. Derivations of Extrinsic and Intrinsic camera parameters- Direct Method (10/03/2022)
10. Indirect Camera Calibration and Image Enhancement in Spatial Domain (14/03/2022)
11. Basics of Spatial Filtering, Smoothing & Sharpening Spatial filters and Laplacian Filters (15/03/2022)
12. Roberts and Sobel Operators, Frequency-Domain Filtering - Notch Filter, Low pass filters (17/03/2022, 9:00-10:00 am)
13. Low pass and High Pass filters, Canny edge detector (17/03/2022, 4:00-5:00 pm)
Assignments
01. Assignment 1 (Posted on 10/02/2022, due on 24/02/2022)
02. Assignment 2 (Posted on 17/02/2022, due on 04/03/2022)
03. Assignment 3 (Posted on 17/03/2022, due on 31/03/2022)
- There will be two to three assignments.
- Late assignments will be accepted, with a 10% penalty per day, up to five days.
- Submission procedure and other requirements will be stated in individual assignments.
- Students are responsible for backing up and protecting their work.
Examinations
01. Mid Term Examination (Held on 02/03/2022, 09:00 to 10:30 am)
02. End Term Examination (Held on 24/04/2022, 09:00 am to 12:00 noon)
Recommended Study Material
The following will be used as a reference/text book for this course:
1. Forsyth, D. A. and Ponce, J., "Computer Vision: A Modern Approach",Prentice Hall, 2nd Ed, 2011.
2. Gonzalez, R. C. and Woods, R. E., "Digital Image Processing",
Prentice Hall, 3rd Ed., 2009.
Instructor: Balasubramanian Raman
Other Instructors: Sanjeev Kumar and Sparsh Mittal
Office: S-227, CSE Building
Class Meeting Time: Mondays, Tuesdays (10:00-10:55 am), Thursdays (9:00-9:55 a.m).
Class Room: Microsoft Teams
Office Hours: Wednesdays, Fridays 11:00 a.m. - 1:00 p.m. and by appointment
TAs: Gaurav Bhardwaj, Puneet Kumar and Phoolpreet
Email: first four letters of first name at cs dot ac dot in
Other Instructors: Sanjeev Kumar and Sparsh Mittal
Office: S-227, CSE Building
Class Meeting Time: Mondays, Tuesdays (10:00-10:55 am), Thursdays (9:00-9:55 a.m).
Class Room: Microsoft Teams
Office Hours: Wednesdays, Fridays 11:00 a.m. - 1:00 p.m. and by appointment
TAs: Gaurav Bhardwaj, Puneet Kumar and Phoolpreet
Email: first four letters of first name at cs dot ac dot in
Announcements
April 24, 2022: End Term Examinations.March 17, 2022: Assignment 3 has been uploaded.
March 10, 2022: Mid-Term Marks have been uploaded.
March 02, 2022: Mid-Term Examinations.
February 17, 2022: Assignment 2 has been uploaded.
February 10, 2022: Assignment 1 has been uploaded.
February 03, 2022: Second session of classes have begun (13 lectures by R. Balasubramanian).
Course Objectives, Learning Outcomes and Prerequisites
To provide a glimpse of what computer vision is about
To give an understanding of image processing for computer vision
To know about Image formation and camera calibration
To impart the knowledge on Feature detection and matching
Course Outcomes:
After completing the course you will be able to:
identify basic concepts, terminology, theories, models and methods in the field of computer vision,
describe known principles of human visual system,
describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition,
suggest a design of a computer vision system for a specific problem
Prerequisites: NIL.
To give an understanding of image processing for computer vision
To know about Image formation and camera calibration
To impart the knowledge on Feature detection and matching
Course Outcomes:
After completing the course you will be able to:
identify basic concepts, terminology, theories, models and methods in the field of computer vision,
describe known principles of human visual system,
describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition,
suggest a design of a computer vision system for a specific problem
Prerequisites: NIL.
Evaluation Components
- Term tests/quizzes (10%)
- Assignments + Practical (10%)
- Class participation (5%)
- Mid-Term Examination (20-30%)
- End Term Examination (40-50%)
Lecture Notes
01. Introduction to Computer Vision (03/02/2022)02. Field of View and Stereo Vision (07/02/2022)
03. Reconstruction of 3D primitives from Stereo and sequence of Images (08/02/2022)
04. 3D Plane and a Straight Line (10/02/2022)
05. 2D Transformations and Viewing in 3D (15/02/2022)
06. One Point, Two point and Three point Perspective Projections, Parallel Projections and Geometric Camera Parameters (21/02/2022)
07. Extrinsic and Intrinsic camera parameters, Perspective and Weak Perspective Camera models (22/02/2022)
08. Epipolar Geometry and camera parameters (24/02/2022)
09. Derivations of Extrinsic and Intrinsic camera parameters- Direct Method (10/03/2022)
10. Indirect Camera Calibration and Image Enhancement in Spatial Domain (14/03/2022)
11. Basics of Spatial Filtering, Smoothing & Sharpening Spatial filters and Laplacian Filters (15/03/2022)
12. Roberts and Sobel Operators, Frequency-Domain Filtering - Notch Filter, Low pass filters (17/03/2022, 9:00-10:00 am)
13. Low pass and High Pass filters, Canny edge detector (17/03/2022, 4:00-5:00 pm)
Assignments
01. Assignment 1 (Posted on 10/02/2022, due on 24/02/2022)02. Assignment 2 (Posted on 17/02/2022, due on 04/03/2022)
03. Assignment 3 (Posted on 17/03/2022, due on 31/03/2022)
- There will be two to three assignments.
- Late assignments will be accepted, with a 10% penalty per day, up to five days.
- Submission procedure and other requirements will be stated in individual assignments.
- Students are responsible for backing up and protecting their work.
Examinations
01. Mid Term Examination (Held on 02/03/2022, 09:00 to 10:30 am)02. End Term Examination (Held on 24/04/2022, 09:00 am to 12:00 noon)
Recommended Study Material
The following will be used as a reference/text book for this course:1. Forsyth, D. A. and Ponce, J., "Computer Vision: A Modern Approach",Prentice Hall, 2nd Ed, 2011.
2. Gonzalez, R. C. and Woods, R. E., "Digital Image Processing", Prentice Hall, 3rd Ed., 2009.