Image Processing (UG)

Module aims

This module will provide students with a substantial introduction to digital image processing relevant to image analysis. It will also provide students with appreciation of aspects of computation in interpreting or “parsing” images, as well as introducing students to some of the biomedical, clinical and research applications of image processing and computer vision

Learning outcomes

Describe and explain image types, representations & basic operations & transformations. Explain the complexity of analysing structured data in 2 dimensions. Describe the potential applications of image processing and computer vision (medical & non-medical). Explain the use of moment calculations and the Hessian matrix to capture information about spatial structure. Comment on some of the relationships between mammalian visual systems and standard computational approaches to vision. Use Linear Image transforms: basis image interpretation, calculation of transforms numerically. Use neighbourhood image operators: Spatial convolution, and the process of mask design. Use Image Segmentation: necessity, approaches and applications. Explain the focus-of-attention in computer vision and use of orientation field descriptors for scale and rotation-invariant patch matching. Explain the importance of scale-space in structured data analysis problems. Load images into MATLAB, manipulating the image display and setting appropriate colour maps. Effectively use some MATLAB image processing tools. Write code for quantitative image processing. Tackle image analysis problems systematically. Systematically identify, label and quantify spatially localised structures using semi-automated methods.

Module syllabus

0. Introductory Lecture 1. Basic Concepts 2. Binary Images 3. Image Transforms 4. Neighbourhood Operator 5. Image Segmentation 6. Image Registration 7. Optional Topic

Pre-requisites

Calculus, derivatives, Linear Algebra. Eigenvalues and eigenvectors of a matrix. Knowledge of random variables would also be useful.

Teaching methods

Students will be taught over one term using a combination of lectures and labs. Lecture sessions will be made available on Panopto for review and supplemented with technologies as appropriate to promote active engagement during the lecture such as 'learning catalytics'. Labs will be based on taught content from lectures to reinforce these topics and allow students to test their understanding. 

Lectures: 20 hours
Labs: 18 hours

Assessments

Overall performance in this module will be assessed by a final exam in the summer term 80% and a Spring term quiz 20%

Outline answers to past papers will be available

Feedback : Feedback is given through lab sessions, and is related to practical skills in tackling programming and image analysis problems.

Reading list