Skip to main content


EEE3013 : Image Processing and Machine Vision

  • Offered for Year: 2022/23
  • Module Leader(s): Professor Satnam Dlay
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semester 2 Credit Value: 10
ECTS Credits: 5.0


To introduce the students to the fundamentals of image processing and provide them with the essential knowledge about the main themes, so that, in the future, the students will be able to readily apply their knowledge in industry or research or further enhance it by self study. The Course is divided into two sections: Image Processing and Machine Vision.

Outline Of Syllabus

Image Processing and Computer Vision Background, Image Processing and Computer Vision
Applications Digital Image Processing Hierarchy: Human Perception of Pictures, Digital Image Processing Hardware. Image Model, Amplitude digitisation: Intensity Quantisation, Spatial coordinate digitisation: Image Sampling, Image Quality, Image Pixel Relationships.

Linear Operators, 2-D Transforms. Spatial Domain Methods (filters), Frequency Domain Methods. Inverse Filtering.

Image. Compression, Redundancy Types, Lossless and Lossy Compression, Image Compression

Object Detection Methods, Edge Liking and Boundary Detection

Thresholding Methods, Region Oriented Methods. Matrix manipulation; Hotelling algorithm, Karhunen–Loève compression

Object Representation and Description, Representation schemes,

Description. Pattern Recognition, Decision Theoretic Methods for Recognition.
Case Study Lectures.

Use of Software such as MATLAB/Python to demonstrate Image processing and Machine Vision Techniques.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture112:0022:002x1hr lectures per week over 11 weeks.
Structured Guided LearningLecture materials50:302:30One online synchronous tutorial every other week from second week, covering tutorial sheets.
Guided Independent StudyAssessment preparation and completion12:002:00Final Exam in Assessment Period
Guided Independent StudyAssessment preparation and completion121:0012:00Revision for final exam
Structured Guided LearningStructured research and reading activities111:0011:00Reading activity to supplement knowledge of material taught in each week.
Scheduled Learning And Teaching ActivitiesWorkshops51:005:00One online synchronous tutorial every other week from second week, covering tutorial sheets.
Guided Independent StudyIndependent study145:3045:30Reviewing lecture notes; general reading
Teaching Rationale And Relationship

Non-synchronous videos and face-to-face lectures provides the core material and synchronous review sessions give students the opportunity to query material taught in that week. Face-to-face lectures can be replaced with online synchronous sessions support by non-synchronous videos if the public health situation requires it.

Problem solving is introduced and practiced through synchronous tutorial sessions.

Assessment Methods

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

Description Length Semester When Set Percentage Comment
Written Examination1202A100Choice of 3 question from 2 from Image processing and 2 from machine vision
Exam Pairings
Module Code Module Title Semester Comment
EEE8098Image Processing and Computer Vision2N/A
Formative Assessments
Description Semester When Set Comment
Written exercise1MTutorial Question on Image Processing set in the middle of the semester
Assessment Rationale And Relationship

Lectures provide the core material as well as guidance for further reading. Problem practice is integrated into lecture structure, examples are given in the use of the software tools.
The examination provides the opportunity for the students to demonstrate their understanding of the course material and their ability to apply critical thinking. The problem solving aspects of the assessment enable the student to demonstrate that they are able to apply this understanding and their analysis and synthesis skills to novel situations.

Reading Lists