MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
Digital Image Processing (FTP_DigImPro)

The goal of this module is to teach the fundamentals of image processing, while putting emphasis on their mathematical and algorithmic principles. In addition, specific 2D and 3D industrial and biomedical applications will be treated.

Prerequisites

Math : basic calculus, linear algebra, probability, derivatives, matrix & vector product, orthogonal bases, eigenvalues, eigenvectors

Programming : good command of any structured programming language (e.g., Python, Matlab, R, Java, C, C++)

Statistics : mean, standard deviation, variance, co-variance, histograms, normal (gaussian) distribution

Signal Processing : Linear&invariant systems, Convolution, 1D-filtering, Sampling, Fourier Transform

Learning Objectives

Upon completion of this lecture, the students should be able to formulate an image processing problem and to propose and pursue alternative ways to it's solution. They can discuss and compare different algorithms and their implementations with regard to robustness, speed and complexity.

Contents of Module

1. Digital Image Fundamentals

  • Linear and nonlinear systems
  • Coordinate systems
  • Geometric transformations
  • Statistics: mean, standard deviation, histograms

2. From 2D to 3D

  • Camera model
  • Epipolar geometry

3. Linear and nonlinear filtering

  • Convolution
  • Correlation
  • Spatial and frequency domain filtering

4. Morphological Image Processing

  • Erosion & Dilatation, Opening and Closing
  • Hit-or-Miss-Transformation (HMT)
  • Connected Filtering

5. Image Segmentation

  • Edge based
  • Region based
  • Intensity based

6. Image  description

  • Boundary descriptors
  • Regional descriptors
  • Texture descriptors
  • Salient points

7. Object Recognition

  • Model based
  • Bayesian classifier
  • Modern methods

Teaching and Learning Methods

Classroom teaching and exercises (paper & with computer)

Literature

Digital Image Processing (Gonzalez & Woods) 4th edition

Download full module description

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