COMP0115 Geometry of Images

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session

2018-19

Module

Geometry of Images

Code

COMP0115

Module delivery

1819/A7U/T2/COMP0115 Masters (MEng)

Related deliveries

1819/A7P/T2/COMP0115 Postgraduate

Prior deliveries

COMPM081

Level

Masters (MEng)

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Arridge, Simon

Contributors

Arridge, Simon

Griffin, Lewis

Module administrator

Ball, Louisa

Aims

To introduce the generalisation of image processing to n-Dimensional data : volume data, scale space, time-series and vectorial data.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. understand the principles of image processing in n-dimensions, time-series analysis and scale space.
  2. understand the relations between geometric objects and sampled images.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MEng Computer Science (International Programme) (Year 4)
  • MEng Computer Science (Year 4)
  • MEng Mathematical Computation (International Programme) (Year 4)
  • MEng Mathematical Computation (Year 4)

Prerequisites:

To be eligible to select this module, students must have:

  • passed BSc/ MEng Computer Science (Years 1 and 2) at UCL; or
  • passed MEng Mathematical Computation (Years 1 and 2) at UCL

And:

Students must also have a strong competency in mathematical and programming skills, including:

  • Fourier Theory (discrete and continuous; sampling; convolution);
  • Calculus (functions of multiple variables; calculus of variationm, integration by parts);
  • Partial Differential Equations (Green’s function methods, finite differencing methods);
  • Matlab programming (multidimensional arrays; image visualisation; anonymous functions)

Students should take the self-test available here http://www0.cs.ucl.ac.uk/staff/S.Arridge/teaching/ndsp/GV11test.pdf to assess their mathematical ability for this course

Content

  • Basic Image Operations
    • Fourier Transforms
    • Convolution and Differentiation in Fourier Domain, Recursive Filters
    • Marching Square/Cubes
    • Level Set Methods
  • Introduction to Differential Geometry
    • Images as functions
      • Definitions
      • Taylor Series expansion and the Koenderick jet
      • Properties of the local Hessian
      • Definition of extrema and saddle points
      • Ridges in n-dimensions
      • Image invarients up to fourth order
    • Curvature
      • Contour curvature
      • Image curvature
      • 3D curvature: the Weingarten mapping, Gaussian and mean curvatures
    • Scale Space
      • Linear Scale Space
        • Introduction and background
        • Formal properties
        • Gaussian kernels and their derivitives
      • Non-linear Scale Space
        • Motivation
        • Edge-effected diffusion (Perona-Malik)
        • Classification of Alvarez and Morel
        • Euclidian and Affine shortening flow
        • Numerical methods for computing scale spaces
    • Multispectral Images and Statistical Classification
      • Feature Space
        • Introduction
        • Definitions of feature space
        • Clustering
      • Statistical Methods
        • Linear and non-linear discriminant functions
        • Supervised learning
        • Unsupervised learning
    • Bayesian and Information Theoretic Approaches
      • Bayesian Image Restoration
      • Markov Random Fields
      • Definitions of Entropy and Mutual information
      • Deconvolution with image priors (statistical and structural)

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures, tutorials, written and programming exercises, and project work.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

75

 

2

Coursework 1

13

 

3

Coursework 2

12

 

In order to pass this module delivery, students must:

  • achieve an overall weighted module mark of at least 50%; and
  • achieve a mark of at least 40% in any components of assessment weighed ≥ 30% of the module.

Where a component comprises multiple assessments, the minimum mark applies to the overall component.