EEE8098 : Image Processing and Computer Vision

Semester 2 Credit Value: 20
ECTS Credits: 10.0


To introduce students to the fundamentals of multimedia systems & image processing and to provide them with essential knowledge about the main themes, so that, in the future, students will be able to readily apply their knowledge in industry or research or further enhance it by self-study.

Outline Of Syllabus

1.       Image Processing and Computer Vision Background, Image Processing and Computer Vision Applications Digital Image Processing Hierarchy.
2.       Human Perception of Pictures, Digital Image Processing Hardware.
3.       Image Model, Amplitude digitisation.
4.       Intensity Quantisation, Spatial co-ordinate digitisation.
5.       Image Sampling, Image Quality, Image Pixel Relationships, Linear Operators, 2-D Transforms.
6.       Spatial Domain Methods, Frequency Domain Methods.
7.       Inverse Filtering.
8.       Image Compression, Redundancy Types, Lossless and Lossy Compression, Image Compression Standard.
9.       Object Detection Methods, Edge Liking and Boundary Detection, Thresholding Methods, Region Orientated Methods.
10.       Boundary Representation and Description, Representation schemes, Description.
11.       Pattern Recognition, Decision Theoretic Methods for Recognition.
Advance material drawn from:
12.       Image Compression & Coding redundancy
13.       Image Compression Models - JPEG
14.       H.261& H.263 video coding standards at low bit rates
15.       MPEG video standards
16.       Case Study Lectures.
MATLAB Image Processing Toolbox.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion400:3020:00Revision for final examination
Guided Independent StudyAssessment preparation and completion13:003:00Final exam
Scheduled Learning And Teaching ActivitiesLecture401:0040:00N/A
Guided Independent StudyDirected research and reading102:3025:00Reading of recommended literature and textbooks
Guided Independent StudyIndependent study1112:00112:00Reviewing lecture notes; general reading
Teaching Rationale And Relationship

Lectures provide core material and guidance for further reading, problem solving practice is integrated into lecture structure. Self-directed learning is through self-study and group presentations.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Description Length Semester When Set Percentage Comment
Written Examination1802A100End of Semester Exam
Exam Pairings
Module Code Module Title Semester Comment
EEE3013Image Processing and Machine Vision1N/A
EEE8028Image Processing and Machine Vision1N/A
Assessment Rationale And Relationship

The examination provides the opportunity for the student to demonstrate their understanding of the course material and their ability to apply critical thinking. The problem solving aspects of the assessment enable students to demonstrate that they are able to apply this understanding and their analysis and synthesis skills to novel situations. There is an in-course assessment through group presentations on a multimedia theme.

Reading Lists