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CODE 80972
LANGUAGE Italian (English on demand)
SEMESTER 1° Semester


The latest electronic technologies and the available computing power allow the use of large quantities of data and digital images, even through economical portable devices. However, while programs and libraries for the enhancement and transformation of digital images are widely available, software for the analysis of digital images and the extraction of information content requires specific skills to understand its processing and for correct and appropriate use .

The course offers an introduction to the world of digital images, focusing on theory and techniques for processing and analysis, with reference to the main applications of medical imaging, remote sensing (satellite images) and colour.



At the end of the teaching, students must have learned the main tools for digital image processing.
In detail:
• Definition of digital signal, digital image, and their representations;
• Knowledge of the main image processing techniques in the spatial domain and in the spectral domain;
• Knowledge of color spaces, CIE standards, color matching and chromaticity diagrams;
• Knowledge of the main pre-processing and filtering techniques such as: histogram transformation, noise models, linear filters and non-linear filters;
• Knowledge of the main edge-detection techniques such as: gradient-based methods, second derivative-based methods and directional derivatives;
• Knowledge of the main segmentation techniques such as: grouping and labeling, statistical methods, region growing;
• Knowledge of Mathematical Morphology applied to binary and grey level images;
• Knowledge of the main techniques used for texture analysis such as: spectral and statistical methods, co-occurrence matrix and fractals.
Furthermore, they must have acquired the ability to choose and use the most suitable techniques for the type of image and type of application (biomedical imaging, radar image, etc.) also thanks to practical laboratory exercises.
In detail:
• Ability to analyze the properties and requirements of different image processing techniques;
• Ability to choose the most appropriate technique based on the application requirements;
• Ability to use different techniques in order to identify and classify objects present in the digital image;
• Ability to define the correct parameters depending on the instrument and application scenario;
• Ability to use Matlab and ImageJ image processing software for image processing and analysis.
In general, the student must prove tha they have acquired the ability to design specific procedures based on the image model and the processing target.


No prerequisite is required.


All educational activities are held in English.

Lectures with the aid of PowerPoint presentations.
Practical SW laboratory exercises.

Lessons are held in person. Attendance, although not mandatory, is recommended. Students who participate in lessons in person are considered to be attending.

We recommend registering on Aulaweb to receive information and download teaching materials.


After an introduction on digital images regarding the definitions of pixels, color channels, quantization and resolution, an in-depth analysis is proposed on some of the most widespread color spaces, the transformation methods between one and the other, with particular reference to colorimetry and perceptually uniform color spaces. The teaching illustrates the fundamental techniques for image enhancement for specific purposes: brightness control, contrast control, thresholding, histogram equalization and noise filtering. In particular, the different solutions for noise reduction based on the noise model, with analysis in the frequency domain and with non-linear approaches are presented.


Digital representation of images
Color spaces
Image filtering (linear and non-linear)
Contour extraction
Mathematical Morphology
Moments and Hough Transform
Texture analysis
Introduction to Deep Learning for Digital Image Processing/Regression/Recognition - Basic concepts - Convolutional Networks - application examples.

Teaching contributes to the acquisition of skills in information technologies and digitalisation, as described in SDG 9 of the UN Agenda 2030 (significantly increase access to information and communication technologies).


C. OLEARI, Misurare il colore, Hoepli, II edizione, 2008

R.M. HARALICK , L:G: SHAPIRO, Computer and Robot Vision, Vol. 1, Addison-Wesley, 1991.

P. ZAMPERONI, Metodi dell'elaborazione digitale di immagini, Masson, 1990.

D. H. BALLARD, C. M. BROWN, Computer vision, Prentice Hall, 1982.

Petrou, Maria MP, and Costas Petrou. Image processing: the fundamentals. John Wiley & Sons, 2010.

Shapiro, L., and G. Stockman. Computer Vision. Prentice-Hall Inc., New Jersey (2001)

Jain, Anli K. Fundamentals of digital image processing. Prentice-Hall Inc., 1989

Class slides can be downloaded from Aulaweb. 

A number of exam exercizes and solutions can be downloaded from Aulaweb.

For non-attending students, students with disabilities and DSA, any revisions to the teaching program will be agreed upon.



Class schedule

The timetable for this course is available here: Portale EasyAcademy



  • The exam consists of two parts to be taken in a single session:

    Written test
    SW practical laboratory test


The written exam will allow to verify the learning results regarding the topics of the program, the orientation and reasoning ability of the student in the field of digital image processing.

The computer-based practical test will verify the ability to use the software seen during the practical laboratory exercises.


Students with disabilities or with DSA can request compensatory/dispensatory measures for the exam. The methods will be defined on a case-by-case basis together with the Engineering Contact of the University Committee for the support of disabled students and those with DSA. Students who wish to request it are invited to contact the class teacher well in advance by copying the Engineering Contact 

(, without sending documents regarding their disability.

Agenda 2030 - Sustainable Development Goals

Agenda 2030 - Sustainable Development Goals
Quality education
Quality education
Industry, innovation and infrastructure
Industry, innovation and infrastructure