Skip to main content

Module

EEE3013 : Image Processing and Machine Vision

  • Offered for Year: 2020/21
  • Module Leader(s): Professor Satnam Dlay
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

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.

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 co-ordinate digitisation: Image Sampling, Image Quality, Image Pixel Relationships, Linear Operators, 2-D Transforms. Spatial Domain Methods, Frequency Domain Methods. Inverse Filtering. Image Compression, Redundancy Types, Lossless and Lossy Compression, Image Compression Standards. Matrix manipulation;Hotelling algorithm, Karhunen–Loève compression, Object Detection Methods, Edge Liking and Boundary Detection, Thresholding Methods, Region Oriented Methods. Object Representation and Description, Representation schemes, Description. Pattern Recognition, Decision Theoretic Methods for Recognition.

Case Study Lectures

MATLAB Iage Processing Toolbox

Teaching Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Assessment Methods

Module leaders are revising this content in light of the Covid 19 restrictions.
Revised and approved detail information will be available by 17 August.

Reading Lists

Timetable