Course Catalogue

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This course introduces fundamental concepts in digital image processing. It recapitulates the mechanics of the human visual system and typical general-purpose image processing system. It discusses several "classical" image processing techniques for intensity transformation and spatial filtering. It helps students develop a solid understanding of how the transform domain can be used for image filtering without becoming a signal processing expert. It also introduces techniques used in image enhancement/restoration/compression/segmentation, object detection and recognition. This course contains laboratory work or open project designs in which participants work in small groups solving real-life problems.

Course will be delivered continuously, 4 hours per week over 12 weeks.

Mandatory Entry Requirements

Knowledge of linear algebra and probability basics

Recommended Entry Requirements

Knowledge of high-level programming language such as C / Python / Matlab

None

None

Final exam (2 hours), 75%

Homework and in-class exercises, 5%

Experiment report/Project report, 20%

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

The assessment of practical skills of this course is via lab working or course projects. Due to the team based nature of course projects, it's impossible to reassess this term. The initial grade on Experiment report/Project report (20%) will be used when calculating the resit grade.

This course aims to provide an overview of fundamental theories, concepts, methods, and digital image processing techniques. It will help students build their image processing toolbox with some essential methods such as vector quantization, histogram equalization, different image transforms, noise reduction, edge detection, morphological operations. It also helps students develop the ability to solve real-life problems.

By the end of this course students will be able to:

• describe how visual information is processed before and after it reaches the end-user; 

• develop an understanding of spatial and transform domains; 

• explain and implement "classical" image processing techniques for image enhancing, restoration, segmentation or compression, and compare their performance;

• combine image processing technique to solve practical image processing or computer vision problems.

Students must attend the degree examination and submit at least 75% by weight of the other components of the course's summative assessment.

Students should attend at least 75% of the timetabled classes of the course.

Note that these are minimum requirements: good students will achieve far higher participation/submission rates. Any student who misses an assessment or a significant number of classes because of illness or other good cause should report this by completing a MyCampus absence report.