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EEE8098 : Image Processing and Computer Vision (Inactive)

  • Inactive for Year: 2024/25
  • Module Leader(s): Prof. Satnam Dlay
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 20
ECTS Credits: 10.0
European Credit Transfer System


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.
12.       PCA - Eigenvalues and eigenvectors
13.       Data reduction
14.       Matrix manipulation
15.       Hotelling algorithm
16.       Karhunen–Loève compression
17.       Example of face recognition using PCA

Advance material drawn from:
18.       Image Compression & Coding redundancy
19.       Image Compression Models - JPEG
20.       H.261& H.263 video coding standards at low bit rates
21.       MPEG video standards
22.       Case Study Lectures.
MATLAB Image Processing Toolbo

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials360:3018:00Non-synchronous recordings to support lectures
Guided Independent StudyAssessment preparation and completion361:0036:00Revision for final exam
Scheduled Learning And Teaching ActivitiesLecture162:0032:004x2hr lectures per week over 4 weeks
Guided Independent StudyAssessment preparation and completion13:003:00Final Exam in Assessment Period
Structured Guided LearningStructured research and reading activities82:0016:00Reading activity to supplement knowledge of material taught in each week.
Scheduled Learning And Teaching ActivitiesWorkshops81:008:001hr online synchronous tutorial per week, covering tutorial sheets.
Guided Independent StudyIndependent study360:3018:00Student study time of non-synchronous pre-recorded material
Guided Independent StudyIndependent study691:0069:00Reviewing lecture notes; general reading
Teaching Rationale And Relationship

Outcomes are achieved through online sessions and present in person teaching.

Lectures provide the core material as well as guidance for further reading. Problem practice is integrated into lecture structure, examples given in the use of the software tools.

Assessment Methods

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

Description Length Semester When Set Percentage Comment
Written Examination1802A100Choice of 4 question from 3 from Image processing and 3 from machine vision
Exam Pairings
Module Code Module Title Semester Comment
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Written exercise2MTutorial Question on Image Processing set in the middle of the semester
Assessment Rationale And Relationship

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