Imaging Technologies

Course description

Image acquisition techniques: digital photography, cameras and illumination units for visible and invisible part of the electromagnetic spectrum, microscopy, radiography, computed tomography, magnetic resonance imaging, ultrasonic imaging, advanced and emerging imaging techniques.

Methods for image restoration, calibration, processing, analysis, integration, measuring and understanding of image content – with the emphasis on robustness, reliability, stability and applicability in real-time.

Design, integration and application of imaging technologies and computer and machine vision systems – in everyday life, in industry and in biomedicine – for the extraction of multidimensional information about the inspected space, objects and subjects.

Course is carried out on study programme

Objectives and competences

To introduce digital image acquisition techniques and methods for image management and image processing for various applications in everyday life, in industry and in biomedicine.

Learning and teaching methods

Teaching is conducted in the form of lectures, which address theoretical methods, the most common technologies and practical examples.

Intended learning outcomes

The understanding of imaging physical backgrounds, technologies and devices for image acquisition, expertise in image restoration and image analysis, and applicable knowledge on imaging systems and technologies.

Reference nosilca

Bulat Ibragimov, Boštjan Likar, Franjo Pernuš, Tomaž Vrtovec

Shape representation for efficient landmark-based segmentation in 3D

IEEE Transactions on Medical Imaging, 2014

Jaka Katrašnik, Franjo Pernuš, Boštjan Likar

A method for characterizing illumination systems for hyperspectral imaging

Optics Express, 21(4):4841-4853, 2013

Miha Možina, Dejan Tomaževič, Franjo Pernuš, Boštjan Likar

Automated visual inspection of imprint quality of pharmaceutical tablets

Machine Vision and Applications, 24(1):66-73, 2013

Primož Markelj, Dejan Tomaževič, Boštjan Likar, Franjo Pernuš

A review of 3D/2D registration methods for image-guided interventions

Medical Image Analysis, 16(3):642-661, 2012

Žiga Špiclin, Boštjan Likar, Franjo Pernuš

Groupwise registration of multi-modal images by an efficient joint entropy minimization scheme

IEEE Transactions on Image Processing, 21(5):2546-2558, 2012

Study materials

[1] Machine Vision: Theory, Algorithms, Practicalities, E. R. Davies, Morgan Kaufmann, 2005.

[2] Handbook of Machine Vision, A. Hornberg, Wiley-VCH, 2006.

[3] Medical Imaging Signals and Systems, J. L. Prince, J. Links, Prentice Hall, 2005.

[4] Digital Image Processing, R. C. Gonzalez, R. E. Woods, Prentice Hall, 2008.

[5] Biomedicinska slikovna informatika in diagnostika, B. Likar, Založba FE in FRI, 2008.

Bodi na tekočem

Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška cesta 25, 1000 Ljubljana

E: T:  01 4768 411