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: 
Date Time Content Module
12 Nov 08.45-10.45 Connector 1.11 Module 1 (Motivation, Image Fundamentals and Signal Transformations), part one
12 Nov 10.45-12.30 Connector 1.11 Computer class (digital images in MATLAB)
15 Nov 13.30-15.30 Paviljoen A 12b Module 1 (Motivation, Image Fundamentals and Signal Transformations), part two
15 Nov 15.30-17.30 Paviljoen A 12b Computer class (exercises)
19 Nov 08.45-11.45 Connector 1.11 Module 2 (Basics of Signals, Sampling, Fourier series, Aliasing, and FIR Filtering), part one
19 Nov 11.45-12.45 Connector 1.11 Computer class (spatial filtering of digital images)
22 Nov 13.30-15.30 Paviljoen A 12b Module 2 (Basics of Signals, Sampling, Fourier series, Aliasing, and FIR Filtering), part two
22 Nov 15.30-17.30 Paviljoen A 12b Computer class (1D signals in spatial and frequency domain)
26 Nov 08.45-10.45 Connector 1.11 Module 3 (Discrete Fourier Transform and Filtering), part one
26 Nov 10.45-12.45 Connector 1.11 Computer class (exercises)
29 Nov 13.30-15.30 Paviljoen A 12b Module 3 (Discrete Fourier Transform and Filtering), part two
29 Nov 15.30-17.30 Paviljoen A 12b Computer class (frequency domain processing)
03 Dec 08.45-10.45 Connector 1.11 Module 4 (Image Restoration and Freq. Filtering & Color Imaging and Transformations), part one
03 Dec 10.45-12.45 Connector 1.11 Computer class (exercises)
06 Dec 13.30-15.15 Paviljoen A 12b Module 4 (Image Restoration and Freq. Filtering & Color Imaging and Transformations), part two
06 Dec 15.30-17.30 Paviljoen A 12b Computer class (restoration and colors)
10 Dec 08.45-10.45 Connector 1.11 Module 5 (Features), part one
10 Dec 10.45-12.45 Connector 1.11 Computer class (exercises)
13 Dec 13.30-15.30 Paviljoen A 12b Module 5 (Features), part two
13 Dec 15.30-17.30 Paviljoen A 12b Module 6 (Segmentation), part one
17 Dec 08.45-10.45 Connector 1.11 Computer class (features)
17 Dec 10.45-12.45 Connector 1.11 Computer class (exercises)
20 Dec 13.30-15.30 Paviljoen A 12b Module 6 (Segmentation), part two
20 Dec 15.30-17.30 Paviljoen A 12b computer class (segmentation)
07 Jan 08.45-10.45 Connector 1.11 Module 7 (Motion analysis & Sports training case study), part one
07 Jan 10.45-12.45 Connector 1.11 Computer class (exercises)
10 Jan 13.30-15.30 Paviljoen A 12b Module 7 (Motion analysis & Sports training case study), part two
10 Jan 15.30-17.30 Paviljoen A 12b Computer class (exercises)
14 Jan 08.45-10.45 Connector 1.11 Computer class (motion)
14 Jan 10.45-12.45 Connector 1.11 Computer class (exercises)
17 Jan 13.30-15.30 Paviljoen A 12b Recap and questions
17 Jan 15.30-17.30 Paviljoen A 12b Recap and questions
Slides: 
Module 1: Motivation, Image Fundamentals and Signal Transformations
Module 2: Basics of Signals, Sampling, Fourier series, Aliasing, and FIR Filtering
Module 3: Discrete Fourier transform and filtering
Module 4: Image restoration and frequency filtering & color imaging and transformations
Module 5: Visual feature extraction
Module 6: Segmentation
Module 7: Motion analysis and sports training case study
Instruction sheets: 
Module 2: Signals, sampling, Fourier series
Module 3: Frequency domain processing
Module 4: Image restoration and color image processing
Exercises: 
Exercises 1: Digital images in MATLAB
Exercises 2: Basics of signals, sampling and Fourier series
Exercise 3: Frequency domain processing
Exercise 4: Image restoration and color image processing