Imaging and Data Processing
| Code | School | Level | Credits | Semesters |
| PHYS4019 | Physics and Astronomy | 4 | 20 | Autumn UK |
- Code
- PHYS4019
- School
- Physics and Astronomy
- Level
- 4
- Credits
- 20
- Semesters
- Autumn UK
Summary
Modern science involves the acquisition of large multi-dimensional datasets, which include time-course signals and images as well as, for example, 4-dimensional data formed by three spatial dimensions and time. The challenge facing experimentalists is to capture these data and process them to extract useful quantitative information. This course aims to provide students with a practical knowledge of the tools required to do this.
On completion of the module, students should be able to: 1) Explain the main categories of data processing, including machine learning and artificial intelligence.
2) Understand and produce computer code to apply these mathematical tools to multi-dimensional data.
3) Have produced presentations and reports in which a problem, theory, methodology and results are presented.
4) Be able to manage their time to meet multiple deadlines.
The major topics covered will include the following:
• Acquisition of large datasets
• Fourier and non-Fourier based data data processing and manipulation
• Data transformation, e.g. rotations, warping
• Automatic recognition of image components (segmentation)
• Blind source separation techniques
• Morphological image processing
• Quantifying information in data (information theory)
• Machine learning and artificial intelligence
The techniques you will learn allow, for example, noisy images to be cleaned up, stars and galaxies to be automatically detected and classified, fingerprints to be classified, quantitative data to be extracted from high dimensional datasets and the amount of information in a signal, image or dataset to be quantified. The course will involve implementation of these methods. It is recommended that either Python or Matlab is used to do this, although other languages are supported.
Target Students
Students in the 4th year of MSci Physics, MathematicalPhysics, CMP and Natural Sciences programmes.
Classes
Module is based on a short set of staff lectures, student presentations and mini-projects.
Assessment
- 30% Coursework 1: Problem Sheets
- 30% Coursework 2: Presentation
- 40% Coursework 3: Project
Assessed by end of autumn semester
Educational Aims
Aims: Modern science often involves the acquisition of large multi-dimensional datasets, which include time-course signals and images as well as, for example, 4-dimensional data formed by three spatial dimensions and time. The challenge facing experimentalists is to capture these data and process them in order to extract useful quantitative information. This course aims to provide students with a working knowledge of the basic techniques required to do this.On completion of the module, students should be able to;explain the main categories of data processing;understand and produce computer code to apply the basic mathematical tools in use for multi-dimensional data processing;have produced reports in which a problem, theory, methodology and results are presented;be able to manage their time to meet deadlines.Learning Outcomes
Knowledge and Understanding
On successful completion of the module, students will have enhanced their:
A1 understanding of how the basic principles of image analysis are applied in a range of situations.
A2 knowledge of Fourier methods for the analysis of physical problems.
A3 knowledge of scientific computing.
A4 knowledge of the fundamental principles and applications of advanced imaging and image processing at, or informed by, the forefront of the discipline.
Intellectual Skills
On successful completion of the module, students will have demonstrated their ability to:
B1 apply theoretical ideas to the quantitative analysis of physical situations.
B2 apply high levels of numeracy and analysis.
B3 apply techniques of problem solving.
B4 apply high levels of computer literacy.
Professional/Practical Skills
On successful completion of the module, students will have demonstrated their ability to:
C1 formulate problems in image analysis using appropriate mathematical language.
C2 carry out independent scientific investigations, including, where appropriate, safety assessments.
C3 model physical problems using appropriate computational methods.
Transferable/Key Skills
On successful completion of the module, students will have demonstrated their ability to:
D1 develop appropriate strategies for study, including the use of library, human, and electronic sources of information.
D2 work successfully both independently and as a member of a small team.
D3 meet deadlines and manage their time effectively.
D4 make effective use of general IT tools for acquiring, processing, and presenting information.
D5 communicate effectively in writing, in particular to report critically on the results of an investigation.
D6 prepare and deliver presentations at a variety of levels and on advanced topics.
D7 effectively manage their time over a range of activities including collaborative working.