Syllabus
Introduction to images; image understanding, image processing, and image generation; sampling and representation; visual perception; image reconstruction; image filters and features; 3D space and transformations; camera models; tracking and pose estimation; lighting and shading; introduction to AR/VR; data visualization and communicating with images.
Tentative Lecture Schedule: :
| Module | Weeks | Topics | PDF
|
| 1 | 1 | Raster/vector images, their representation, and acquisition
| Lec 1
|
| 2 | 1 | Color theory, human vision and color perception
| Lec 2 ,
Lec 3 Part I
|
| 3 | 2,3 | Images as sample, resolution, sampling and reconstruction
| Lec 3 Part II,
Lec 4,
Lec 5
Lec 6
|
| 4 | 3 | Image normalization, histograms, contrast enhancement
| Lec 10, Lec 11-1
|
| 5 | 3-5 | Image processing tools (filters, frequency filters, gradients, etc.)
| Lec 7,
Lec 8
Lec 9
|
| 6 | 5-7 | Image features including edges and corners
| Lec 11-2
Lec 12
Lec 13
|
| 7 | 7,8 | Transformations, camera models, calibration, projective geometry
| Lec 13
Lec 14
Lec 15
Lec 16
|
| 8 | 8,9 | 3D shapes and models
| Lec 20B
|
| 9 | 9-11 | Tracking and pose estimation and their applications to augmented reality and robotics
| Lec 17
Lec 18,
Lec 19
Lec 20A
|
| 10 | 11 | Lighting, using interpolation, properties of natural lighting
|
| 11 | 11,12 | Rendering with OpenGL-style graphics pipeline and shaders with application to virtual reality
|
| 12 | 12,13 | User interfaces and interaction with machines
|
| 13 | 13,14 | Visualization of scalar/vector fields, time-varying, high-dimensional data. exploration and presentation.
|
Academic Integrity Code
Academic honesty is required in all work you submit to be graded.
You must solve all programming assignments entirely on your own,
except where group work is explicitly authorised. This means you
must protect your code solutions, and work so that others cannot
access them. This also means that you may not use any fragment
of code obtained online, except when these are explcitly authorised
as a part of the course. When you do this such code must be clearly
identified with due credit to the source.
Falsifying program output or results is dishonest. Please see your
professor if there are any questions about what is permissible.
Students who are caught cheating in an assignment will incur a -7 penalty.
A repeat will lead to F, as will cheating in a written exam.
for disciplinary action.
Grading Policy
- Completion of assignments is required. Attendance is required.
- +2 for class participation and best 3 quizzes taken if attendance ≥ 90%
- A total delay of up to 120 hours across assignments is condoned; 1 mark penalty per day (or part) thereafter.
- To obtain D: 4 in Quizzes, 8 in assignments, and 18 in minor+major. Or, 35 marks total.
- To obtain E: 22 marks total
- AUDIT PASS: pass each category, and 45 total marks
Assignments & Grading
| Work | Points | Tentative Schedule
| | Assignment 1 | 7 | Due Aug 17, 11pm.
|
| Assignment 2 | 7 | Due Sep 7 12, 11pm.
|
| Assignment 3 | 7 | Due Oct 1121, 11pm.
|
| Assignment 4 | 7 | Due Nov 17, 11pm.
|
| Quizzes (including class participation) | 12 | Not announced
|
| Mid-term Exam | 30 | TBA
|
| End-term Exam | 30 | TBA
|
Recommended Text
Aditi Majumder, M. Gopi, Introduction to Visual Computing Core Concepts in Computer Vision, Graphics, and Image Processing.. (CRC Press)
Peter Shirley, Steve Marschner, Fundamentals of Computer Graphics. (Pearson)
Rafael Gonzalez, Richard Woods, Digital Image Processing. (Pearson)
David Forsyth, Jean Ponce, Computer Vision: A Modern Approach. (CRC Press)
Richard Szeliski, Computer Vision: Algorithms and Applications. (Springer)
Reading Material
