LEARNING OUTCOMES

The course provides an introduction to digital image processing techniques. Analysis of digital images has several important applications e.g. remote sensing, biomedical imaging, telecommunications, character recognition, advertising photography, historical objects analysis. Nowadays, the available computational power allows almost everyone to leverage on high-performance algorithm for image processing.

In the first part, digital images will be introduced. Several color spaces are described and common techniques to change from one to another are provided.

Basic methods are presented, e.g. contrast enhancement, thresholding, histogram analysis, noise reduction, underlining the use of the discrete Fourier transform (DFT).

Classical techniques for edge detection, segmentation, mathematical morphology analysis, texture analysis are topics of the course.

During practical lessons, software for image processing such as GIMP, ImageJ, MatLab and libraries such as come OpenCV are used.

PREREQUISITES

No prerequisite is required.

TEACHING METHODS

Combination of traditional lectures and mandatory laboratory exercises.

SYLLABUS/CONTENT

• Digital Image Representation
• Color Spaces
• Image Filtering (linear and non-linear)
• Edge detection
• Image Segmentation
• Mathematical morphology
• Moments and Hough Transform
• Texture analysis

RECOMMENDED READING/BIBLIOGRAPHY

• Jhane, B., Digital Image Processing, Sprinter, Berlin, 1997 
• Dellepiane S.,  (2004), Elaborazione di immagini digitali – Materiale di supporto alle lezioni ed Esercizi. GENOVA: ECIG, ISBN: 88-7544-017-4
• Class slides can be downloaded from aulaweb. 
• Tests and solutions can be downloaded from aulaweb.