5XSF0 - Sport, Technology and Behaviour

Goal
Become acquainted with the theory and main concepts of digital signal and analysis; make a choice of an approach for a given basic signal/image processing problem; implement the chosen approach in MATLAB and critically evaluate the result; be able to operationalize theoretical concepts from signal/image analysis in a hands-on case study.

Contents
Signal sensing and health/medical imaging is the most important discipline for healthcare diagnosis. Besides this, video cameras play a very important role in the behavior analysis of people in real life and during sports exercises. This course provides in-depth knowledge on signal processing and image processing techniques and the principles of video signal analysis. This course starts with an introduction to 1-D signal sampling and the extension to multi-dimensional signal processing for images and video. Then the course proceeds with the principles and definitions for the frequency-domain representation of 1-D signals and 2-D images and the possibilities for filtering and other processing possibilities in the frequency domain. In a further module, the focus is on the signal and image quality enhancement in various ways. Various noise models are discussed and filtering approaches for reducing the noise. The introduction of color in images for improved understanding is addressed. The second part of the course provides an introduction to image understanding and object/area segmentation to learn 1-D signal and 2-D imaging for health diagnosis. This part starts with simple binary operators and then extends to the detection of points, lines, edges, etc. Secondly, specific region-based segmentation methods are discussed. Features are extracted from images, which is addressed in both the spatial domain directly in the image and a frequency-oriented domain, such as the Gabor transformation. The course concludes with a module on imaging techniques and use cases, where the learned techniques are applied. These cases include computer-aided cancer diagnosis, instrument detection, etc.

Schedule and location: 
8 Weeks of lectures in 2 blocks of 4 hours
Full schedule: 
  • 14 Nov 08.45-10.30 Flux 1.09 : Module 1
  • 14 Nov 10.45-12.30 Flux 1.09 : Computer class (digital images in MATLAB)
  • 17 Nov 13.45-15.30 Matr 1.46 : Module 1
  • 17 Nov 15.45-17.30 Matr 1.46 : Computer class (exercises)
  • 21 Nov 08.45-10.30 Flux 1.09 : Module 2
  • 21 Nov 10.45-12.30 Flux 1.09 : Module 2
  • 24 Nov 13.45-15.30 Matr 1.46 : Computer class (spatial filtering of digital images)
  • 24 Nov 15.45-17.30 Matr 1.46 : Computer class (exercises)
  • 28 Nov 08.45-10.30 Flux 1.09 : Computer class (1D signals in spatial and frequency domain)
  • 28 Nov 10.45-12.30 Flux 1.09 : Module 3
  • 01 Dec 13.45-15.30 Matr 1.46 : Module 3
  • 01 Dec 15.45-17.30 Matr 1.46 : Computer class (exercises)
  • 05 Dec 08.45-10.30 Flux 1.09 : Module 4
  • 05 Dec 10.45-12.30 Flux 1.09 : Module 4
  • 08 Dec 13.45-15.30 Matr 1.46 : Computer class (frequency domain processing)
  • 08 Dec 15.45-17.30 Matr 1.46 : Computer class (exercises)
  • 12 Dec 08.45-10.30 Flux 1.09 : Module 5
  • 12 Dec 10.45-12.30 Flux 1.09 : Computer class (restoration and colors)
  • 15 Dec 13.45-15.30 Matr 1.46 : Module 5
  • 15 Dec 15.45-17.30 Matr 1.46 : Computer class (exercises)
  • 19 Dec 08.45-10.30 Flux 1.09 : Module 6
  • 19 Dec 10.45-12.30 Flux 1.09 : Computer class (features)
  • 22 Dec 13.45-15.30 Matr 1.46 : Module 6
  • 22 Dec 15.45-17.30 Matr 1.46 : Computer class (exercises)
  • 09 Jan 08.45-10.30 Flux 1.09 : Computer class (segmentation)
  • 09 Jan 10.45-12.30 Flux 1.09 : Computer class (exercises)
  • 12 Jan 13.45-15.30 Matr 1.46 : Module 7
  • 12 Jan 15.45-17.30 Matr 1.46 : Module 7
  • 16 Jan 08.45-10.30 Flux 1.09 : Recap and questions
  • 16 Jan 10.45-12.30 Flux 1.09 : Computer class (exercises)
  • 19 Jan 13.45-17.30 Matr 1.46 : Computer class (exercises)
  • 23 Jan 13.30-16.30 : Final exam