Prosiectau Ffiseg ar gyfer myfyrwyr bl.3 a bl.4

Disgrifiad prosiect

Parametrising Transient Phenomena From Image Sequences

(supervisor: Tom Knight)

Nature of project: experimental, experimental

Available to students on full-time physics degree schemes or joint students.

Project description and methodology

Transient phenomena captured in video files (or image sequences) are commonly studied by physicists and other scientists. Extracting information from these sequences can be challenging and is always time-consuming. The project focuses on automatic identification and parametrisation of the properties of objects that vary frame-to-frame. Applications would include bouncing balls, deformable object collisions (like water balloons), feature growth (like cancer cell clusters), etc.

The objective is to capture transient phenomena in video and write software that analyses the image sequences for identifiable entities, quantify properties (at the very least, position) of the entities and track those properties for each entity through the image sequence.

A successful project will develop beyond the above in one/some of the following directions:
Is it possible to extrapolate from the observed properties to correctly identify an entity that was hidden for part of the image sequence, like a bouncing ball briefly obscured?

When considering where to take your project, please bear in mind the time available. It is preferable to do fewer things well than to try many and not get conclusive results on any of them. However, sometimes it is useful to have a couple of strands of investigation in parallel to work on in case delays occur.

Additional scope or challenge if taken as a Year-4 project: Should be developed as applied to high deformation objects. Additional properties of the tracked entities would need to include parametrisation of the shape of the entity, and using that to analyse the area and/or rotation of the entities.

Please speak to Tom Knight (tmk08) if you consider doing this project.

Initial literature for students:

  1. Alexander Mordvintsev & Abid K., 2013, OpenCV Python Tutorials
  2. John C. Russ 2011, The image processing handbook, 6th ed.
  3. Module PH24010 (Data Handling and Statistics)
  4. http://scikit-image.org

Novelty, degree of difficulty and amount of assistance required

Programming heavy project. Previous python (or other object orientated language) experience would be useful though not necessary.

Difficulty is dependent on the student's pace through the project and the chosen application.

Project milestones and deliverables (including timescale)

milestoneto be completed by
Familiarity with python and external librariesend of October
Simple application of library to simplified example image/image sequenceend of November
Position (and other properties?) extractionend of February
Analysis of Parameters and/or Refinement of extractionEaster

Students taking this project will have to submit a full risk assessment form