Course description
Human vision: eye structure, image formation, adaptation to light, space and scene, resolution, colour sensing, optical illusions, image interpretation and understanding.
Digital images and videos: representations and definitions, space and time sampling, quantization, image acquisition technologies, quality parameters.
Visualization and manipulation: visualization of greyscale, colour and multi-dimensional data via cross-sections and projections, surface and volume rendering, intensity and geometric transformations, arithmetic operations.
Compression: compression fundamentals, coding, spatial and temporal redundancy, irrelevant information, measuring image information and quality, compression systems, formats and standards.
Processing, restoration and analysis: filtering and quality improvement, morphological image processing, colour image processing, fundamentals of segmentation, quantitative analysis and image understanding.
Applications: in general use, in industry and in biomedicine for the acquisition of multi-dimensional information about space, objects and subjects.
Course is carried out on study programme
Electrical engineering 1st level
Objectives and competences
The aim of the subject is to introduce basic properties of human vision and the technologies for digital image acquisition, visualization, manipulation, compression, processing, and for problem solving in general use, in industry and in biomedicine.
Learning and teaching methods
Basic theory, procedures and practical examples are considered at lectures, while practical knowledge is gained through problem-solving tasks at lab works.
Intended learning outcomes
After successful completion of the course, students should be able to:
- define basic properties of human vision
- explain digital image visualization principles
- process digital images with personal computer
- analyse digital images with personal computer
- evaluate quality of digital images
- describe basic building blocks of image compression methods
- explain applicability and information content of images
Reference nosilca
1. Bulat Ibragimov, Boštjan Likar, Franjo Pernuš in Tomaž Vrtovec, Shape representation for efficient landmark-based segmentation in 3D, IEEE Transactions on Medical Imaging, 2014.
2. Jaka Katrašnik, Franjo Pernuš in Boštjan Likar, A method for characterizing illumination systems for hyperspectral imaging, Optics Express, 21(4):4841-4853, 2013.
3. Miha Možina, Dejan Tomaževič, Franjo Pernuš in Boštjan Likar, Automated visual inspection of imprint quality of pharmaceutical tablets, Machine Vision and Applications, 24(1):66-73, 2013.
4. Primož Markelj, Dejan Tomaževič, Boštjan Likar in Franjo Pernuš, A review of 3D/2D registration methods for image-guided interventions, Medical Image Analysis, 16(3):642-661, 2012.
5. Žiga Špiclin, Boštjan Likar in 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. R.C. Gonzalez, R.E. Woods, Digital Image Processing, Prentice Hall, 3rd edition, 2008.
2. B. Likar, Biomedicinska slikovna informatika in diagnostika, Založba FE in FRI, 1. izdaja, 2008.
3. Elektronsko gradivo – prosojnice predavanj in navodila za vaje: http://lit.fe.uni-lj.si/SI