Methods for image processing

A.Y. 2020/2021
Overall hours
Learning objectives
The aim of this course is to provide the general principles on the acquisition, the representation, and the improvement of digital images and the processing techniques for extracting information from images of real scenes.
Expected learning outcomes
The student will know the usage of basic techniques of image processing and analysis for:
· image quality improvement;
· information extraction from images;
· compression and representation of images.
Moreover, the student will be able to interpret their role in more advanced techniques.
Course syllabus and organization

Single session

Lesson period
First semester
Teaching methods
The lessons will be held on the Microsoft Teams platform and can be followed both synchronously based on the timetable of the first semester and asynchronously as they will be recorded and uploaded on that same platform.

Course syllabus and Teaching Resources
The contents and reference material will not be changed.

Assessment methods and Criteria
The final exam will be an oral test through the Microsoft Teams platform, possibly also using tools available on other platforms.
Course syllabus
The course concerns the funding concepts of the digital image processing. The lectures will introduce the principles of the processing of digital signals, the sampling, and encoding, the techniques generally used in image processing: geometrical operations, features extraction, equalization, filtering, transforms, image encoding and compression. Laboratory sessions will also take place in which numeric simulation software
will be used.
· Introduction: introduction to the image processing, image basic concepts.
· Digital images fundamentals: light, vision and perception; acquisition and digitalization of images.
· Image representation: formats for the representation of digital images, pixel relations, basic mathematical operations.
· Intensity transforms and spatial filtering: intensity transforms, histograms, equalization, spatial domain filtering, equalization, image improvement in spatial domain.
· Filtering in the frequency domain: Discrete Fourier Transform, extension to 2D functions, filtering
and improvement of images in the frequency domain.
· Morphological image processing: dilation, erosion, opening, closing, extraction of connected
components, convex hull, thinning, thickening, contour extraction.
· Image segmentation: edge detection and linking, region based processing.
· Image compression: redundancy, image encoding.
Prerequisites for admission
Some topics studied in the course requires the knowledge of the fundamentals of calculus, probability and statistics, and programming.
Teaching methods
Teaching Resources
R.C. Gonzalez and R.E. Woods, Digital Image Processing, (3 ed.), Prentice Hall, 2008. ISBN 9780131687288.
Assessment methods and Criteria
The assessment will be an oral exam.
The oral exam consists in three questions (the first of which is chosen by the student) to cover all the topics of the syllabus, using also problems and exercises.
The student can prepare few slides to help the presentation of the chosen topic.
Besides the competence on the topics, the evaluation considers the clarity of the presentation and the comprehension of the problems.
The final score is expressed in thirtieths.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor: Ferrari Stefano
Educational website(s)