L'insegnamento prevede 1.0 CFU erogati con Didattica Innovativa come segue:
Blended Learning & Flipped Classroom
Corso di Studi
Codice Piano di Studio preventivamente approvato
Ing Ind - Inf (Mag.)(ord. 270) - MI (263) MUSIC AND ACOUSTIC ENGINEERING
091042 - VIDEO SIGNALS
Ing Ind - Inf (Mag.)(ord. 270) - MI (474) TELECOMMUNICATION ENGINEERING - INGEGNERIA DELLE TELECOMUNICAZIONI
054316 - VIDEO SIGNALS
054318 - AUDIO AND VIDEO SIGNALS
Ing Ind - Inf (Mag.)(ord. 270) - MI (487) MATHEMATICAL ENGINEERING - INGEGNERIA MATEMATICA
095978 - AUDIO AND VIDEO SIGNALS
Visual information plays an important role in almost all areas of our life. Today, much of this information is represented and processed digitally. Digital image processing is ubiquitous, with applications ranging from television to tomography, from photography to printing, from robotics to remote sensing. "Video Signals" is a graduate-level introductory course to the fundamentals of digital image processing. The goal is to provide the students with the knowledge to handle algorithms for visual information processing and to develop novel approaches for specific applications.
Risultati di apprendimento attesi
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
· Deal with image and video signals.
· Model the human visual system and how it is replicated in the cameras.
· Analyze visual information in different domains (frequency analysis, morphological descriptors) and specific filters.
· Deal with colors and hyperspectral images.
· Extract features and descriptors from images in order to describe effectively their content for classification and recognition purposes
Given a relatively complex problem, students will be able to:
· Analyze and understand the goals, assumptions and requirements associated with the problem.
· Define the algorithmic procedure to solve the problem (e.g., choice of a suitable solution, parameters estimation and tuning, etc.)
Students will learn to:
· Describe image processing algorithms highlighting strengths and weaknesses of different approaches.
· Present their work abstracting from the selected implementation
Main course topics:
image sampling and quantization,
pinhole and real cameras models,
colors and colorimetry,
spatial filtering and local descriptors,
Object detection, Hough/Radon transforms,
morphological image processing,
image Spectral analysis based on Fourier transform,
noise reduction and restoration,
deconvolution and blind deconvolution,
Image and Video Compression,
motion analysis and tracking,
multiple image stitching for panoramic images and videos,
High Dynamic Range Images.
Laboratory activities enable student to improve their understanding of the concepts learnt during the lectures.
The proposed examples of applications and exercises are based on Matlab® and the related toolboxes (Digital Signal Processing System Toolbox,Image acquisition toolbox, Image Processing Toolbox ).
The "Flipped Classroom" methodology will be applied to practical aspects concerning better understanding of Matlab techniques for a proper image processing.
Students are required to know the basic principles of digital signal processing.
Modalità di valutazione
The exam for the “video signals” module is a written test with 3 or 4 exercises to be solved in 2 hours. Two or three exercises require a numerical/procedural solution while the last one is a exercise requiring the definition of a Matlab procedure in order to solve the question and will focus on exercises exposed during laboratories. The Matlab code has to be written directly on the paper without any computer aid.
The exam for the first module will take place only in the written version; no oral exams, integrations or projects will be considered in order to increase the obtained grade.
Concerning the assessment of the whole exam (Audio and Video Signals)
In order to pass the whole “Audio and Video Signals” exam (first and second module) (for students that are not following just the Video module, a positive grade must be obtained in both modules and the final mark will be the average of the two marks rounded up the next integer.
In evaluating the average between the two modules a 30 cum laude mark in a module will be considered as 30; in order to get 30 cum laude as a final mark, the mark of at least one module must be 30 cum laude.
In every exam date the two modules will take place the same day one after the other (more details will be provided by the web poliself); however students can choose to take on a single module or both modules in the same day.
Once a student gets a positive mark in a module this mark will be automatically frozen until she/he takes again the same module in a following session; “taking again the exam on the same module” means that the student registers, participates and turns in his/her solution; while if the student simply participates to an exam but does not turn in his/her solution, this will not change the previous mark and the student will be considered as "asbsentee".
Once a student gets a positive mark in both modules the final mark will be evaluated and then published for the publishing period on the web poliself ; at the end of this period it will be automatically recorded. If the student does not want to record that final grade he/she has to refuse it from the poliself: in that case both marks will be restored in a "frozen" state in order to allow the student to take on again a module (or both of them). However if the student has a frozen mark in both modules at the end of each exam to which his/her signed in, the final average mark will be automatically published and, after the publishing period, automatically recorded: so, if the student wants to improve the final mark he/she has to remember to refuse the published final grade.
Type of assessment
Solution of numerical problems concerning:
· Image correction
o Brightness/contrast enhancement
o Distortions removal
o Colors correction
o Change of perspective
· Image filter
· Edge/corner extraction
· Application of morphological operators
· Denoising/Wiener Filtering
· Application of segmentation criteria
· High Dynamic Range Images fusion
Exercises focusing on design aspects
· Image correction (opeartions on histogram, colors, distortions…)
· Processing based on frequency analysis
· Implementation of ad-hoc Morphological operators
· Features extraction and comparison.
· Hough and Radon transform
· Perspective correction
· Deconvolution and blind deconvolution
Theoretical questions on all course topics with open answer
· image sampling and quantization,
· pinhole and real cameras models,
· colors and colorimetry,
· spatial filtering and local descriptors,
· Object detection, Hough/Radon transforms,
· image segmentation,
· morphological image processing,
· image Spectral analysis based on Fourier transform,
· noise reduction and restoration,
· deconvolution and blind deconvolution,
· Image and Video Compression,
· motion analysis and tracking,
· multiple image stitching for panoramic images and videos,
· High Dynamic Range Images.
R.C. Gonzalez, R. E. Woods, Digital Image Processing, Editore: Addison-Wesley Pub., Anno edizione: 2008
Nixon, Mark Aquado, Alberto S Book., Feature Extraction and Image Processing , Anno edizione: 2002, ISBN: 0750650788
Bankman, Isaac, Handbook of Medical Imaging , Editore: Elsevier Academic Press, Anno edizione: 2000, ISBN: 0120777908
Mallat, Stephane, Wavelet Tour of Signal Processing, Editore: Academic Press, Anno edizione: 1999, ISBN: 0-12-466606-X
Theodoridis, Sergios Koutroumbas, Konstantinos., Pattern Recognition, Editore: Elsevier Academic Press, 2003., ISBN: 0126858756
Tipo Forma Didattica
Ore di attività svolte in aula
Ore di studio autonome
Laboratorio Di Progetto
Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese