CSCI3290 Computational Imaging and Vision


Course code CSCI3290
Course title Computational Imaging and Vision
Course description It is an emerging new field created by the convergence of computer graphics, computer vision and machine learning. Its main purpose is to overcome the limitations of the traditional camera by using computational techniques to produce a richer, more vivid, perhaps more perceptually meaningful representation of our visual world. The content of this course is to study ways in which samples from the real world (images and video) can be used to generate compelling computer imagery. We will learn how to acquire, represent, and render scenes from digitized photographs. The following topics will be covered: cameras, image formation and models; image manipulation (warping, morphing, mosaicing, matting, compositing); data-driven synthesis; visual perception; high dynamic range imaging and tone mapping; image-based lighting; non-photorealistic rendering; and other applications in machine vision.
Unit(s) 3
Course level Undergraduate
Semester 1 or 2
Grading basis Graded
Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
Learning outcomes At the end of the course of studies, students will have acquired the ability to
1. understand the problem and emerging techniques of computational photography;
2. know the methods for generating compelling pictures and understanding images in computer vision;
3. use software packages, such as Python, C++, or Matlab.
(for reference only)
Assignments: 70%
Exam: 30%
Recommended Reading List 1. Computer Vision: The Modern Approach, Forsyth and Ponce, Prentice-Hall, 2002.
2. Photography, 8th edition, London and Upton, Prentice Hall, 2004.
3. Vision Science: Photons to Phenomenology, Stephen Palmer, The MIT Press, 1999.


CSCIN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate, and solve computer science problems (K/S); TP
2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); TP
4. communicate effectively (S/V);
5. succeed in research or industry related to computer science (K/S/V);
6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S);
7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);
8. practise high standard of professional ethics (V);
9. draw on and integrate knowledge from many related areas (K/S/V);
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured