MSE Master of Science in Engineering

The Swiss engineering master's degree

Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie: 

  • 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
    I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati.
  • 9-12 crediti ECTS in basi teoriche ampliate (FTP)
    I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione 
  • 6-9 crediti ECTS in moduli di contesto (CM)
    I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.

La descrizione del modulo (scarica il pdf) riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:

  • Insegnamento
  • Documentazione
  • Esame
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.

Requisiti

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

Obiettivi di apprendimento

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.

Contenuti del modulo

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

Metodologie di insegnamento e apprendimento

Classroom teaching and exercises (paper & with computer)

Bibliografia

Digital Image Processing (Gonzalez & Woods) 4th edition

Scarica il descrittivo completo del modulo

Indietro