Methods for Image Processing
A.Y. 2024/2025
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.
Making judgements
The student will be able to evaluate the impact of the studied techniques for each task and which of them will be more effective, given the constraints of the problem.
Learning skills
The knowledge of the fundamental techniques for image processing allows the student to further deepen the study of advanced techniques, framing and relating them with the constraints of given application domains.
· 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.
Making judgements
The student will be able to evaluate the impact of the studied techniques for each task and which of them will be more effective, given the constraints of the problem.
Learning skills
The knowledge of the fundamental techniques for image processing allows the student to further deepen the study of advanced techniques, framing and relating them with the constraints of given application domains.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
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.
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
Lectures
Teaching Resources
R.C. Gonzalez and R.E. Woods, Digital Image Processing, (4 ed.), Pearson, 2018. ISBN 9780133356724.
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.
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
Shifts:
Turno
Professor:
Ferrari StefanoEducational website(s)
Professor(s)