CONTENTS 1. Introduction 1.1 Course Outline 1 1.2 Introduction ...

Indicative syllabus content:
• Sampling and properties of digital images
• Digital Filtering
• Edge detection and image segmentation
• Non-linear filtering
• Statistical Processing
• Image analysis
• Geometric Operations
• Image Transforms
• Image restoration
• Image compression techniques
• Compression in the temporal domain
• Computer vision / image recognition
Teaching and Learning Methods:
The module is of 12 taught week’s duration in semester 1 with a 1 hour theory lecture and two
hours supervised laboratory time per week. Teaching methods will include illustrated lectures,
seminars, presentations and practical demonstrations. Practical work will involve supervised
laboratory work that may involve both group projects and individual investigations. Written
coursework may involve essays, derivations and calculations or computer-based exercises.
Assessment Rationale:
The examination is unseen and will test students’ ability to define concepts, derive results,
describe applications and procedures and to analyse and comment on specific case
studies.(Learning outcomes 1,2,4,5,6)
Written Coursework may consist of an essay or a number of problems requiring a detailed and
accurate analysis at the appropriate level, with justifications and appropriate assumptions.
Students may consult a range of sources and assignments are timed to enable valuable
feedback.(Learning outcomes 1,2,4,5,6,8)
Laboratory work is assessed by written reports from practical assignments. The student must
demonstrate an ability to:
• Understand and interpret an experimental brief and establish the appropriate experimental
design
• Produce relevant and operational MATLAB programmes for a range of specific tasks.
• Undertake reliable collection of data using suitable instrumentation.
• Analyse the data in an accurate manner, and clearly present the relevant results.
• Present sound discussions and conclusions on the work, in the light of other published results
or expected outcomes.
(Learning outcomes 1,2,3,5,6,7,8)
Assessment criteria:
The extent to which the student is able to demonstrate an ability to:
• Derive important relationships between concepts from the theory.
• Prove important theoretical results from first principles or as directed.
• Interpret the terminology relevant to image quality.
• Use appropriate procedures from theory to analyse data and interpret findings.
• Plan, carry out and report on experimental investigations.
• Comment critically on results obtained, with due regard for experimental errors and the
significance of the result.
• Use research/reference literature as appropriate.
DPI_Hbook 90 ©University of Westminster