This is a computer science elective unit created for the revised BCS and BSE degrees following the faculty restructure of undergraduate degrees. It is based on the previously taught unit CSE3314 Image Processing.

20/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

25/01/2018 - Admin: fixing calculation of workload hours.

10/11/2017 - Admin: adding RfC on behalf of CE - Amended the Workload/ Contact Hours since this unit is more effective in studio mode where the students can explore Image Processing library functions for different algorithms in MATLAB at the same time the lecture is conducted.

14/02/2017 - Amended the wording of hurdle requirement on advice from UGPC Chair and Director Education Quality. Consulted with CE on the amendment.

10/02/2017 - Admin: updated on behalf of CE. CE has refreshed this unit for offering in Malaysia in 2017.

11 Nov 2013 - Change was made to increase the practical hour from 1 hour to 2 hour and eliminating 1 hour tutorial because one hour is insufficient to run a lab session.

9 April 2013 - ASCED code updated as per request from UGPC 1/13, Item 5.2. Code as advised by email from Loke Kar Seng on 7/3/2013. Change effective from Semester 1, 2014.

This unit has been updated as part of a 2009 review and comparison of data with current Handbook, Syllabus + and Callista data.

18/09/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

This is a third year level unit dealing with the processing and analysis of pictorial information. It is a specified computer science elective unit suitable for any student with appropriate prerequisite knowledge and skills in Mathematics and Programming. This unit is intended to introduce to students to the methods and tools of processing and analysis of discrete image data from an algorithmic and mathematical viewpoint.

Image Processing is an integral part of the ACM-IEEE Computer Science undergraduate curriculum (Classified under the category GV11: Computer Vision). This unit plays the key role of introducing students to techniques of visual information processing and analysis.

At the completion of this unit students will be able to:

1. explain the processes of image formation, acquisition, processing and analysis;
2. explain the type of algorithm required for a particular image processing task among a wide range of available methodologies;
3. develop programs for manipulating grey level and colour images using standard image processing algorithms;
4. develop and analyse software for image segmentation, image classification, image data mining, and computer vision;
5. develop algorithms to extract and analyse features in medical, document, and other images;
6. participate in a team as an image processing specialist communicating with other team members to develop image processing software.

020199

This unit introduces fundamental image processing techniques for the digital manipulation of 2D image data. Algorithms explored include those for edge detection, image enhancement, feature and shape extraction, segmentation and noise removal. The unit provides students an opportunity to develop theoretical understanding of these algorithms, and practical skills in implementing and applying them to real image data.

Software

*MATLAB Prescribed text(s)

Limited copies of prescribed texts are available for you to borrow in the library.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, Pearson; 4th edition, 2017

R. C. Gonzalez and R. E. Woods, Digital Image Processing Using Matlab, 2nd Edition, 2009

On-campus

Laboratory-based classes, hands-on sessions and problem-based learning This teaching and learning approach provides facilitated learning, practical exploration and peer learning. ​The unit runs as Laboratory-based classes/ workshops providing the students to explore Image Processing algorithms using Problem-based learning. Compared to most of the other units, this unit involves a lot of MATLAB programming during workshops as well as required for assignments. Thus students are recommended to install MATLAB software in their laptops.

Examination (2 hours and 10 minutes): 50%; In-semester assessment: 50%

Hurdle requirements: In addition to the standard Faculty Hurdle requirements, students must submit/present all the assignments in order to pass the unit.

1. Assignment 1: - 10% - ULO: 1, 2, 3, 4, 5, 6
2. Assignment 2: - 25% - ULO: 1, 2, 3, 4, 5, 6
3. Class participation: - 15% - ULO: 1, 2, 3, 4, 5, 6
4. Image Processing - PAPER 1: - 50% - ULO: 1, 2, 3, 4, 5

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Additional requirements (all students):

FIT

FIT2004 (or CSE2304)