IMAGE PROCESSING

Teaching in italian
IMAGE PROCESSING
Teaching
IMAGE PROCESSING
Subject area
ING-INF/03
Reference degree course
COMPUTER ENGINEERING
Course type
Laurea Magistrale
Credits
9.0
Teaching hours
Ore Attività frontale: 81.0
Academic year
2018/2019
Year taught
2018/2019
Course year
1
Language
INGLESE
Curriculum
PERCORSO COMUNE
Reference professor for teaching
DISTANTE Cosimo
Location
Lecce

Teaching description

Teaching program is provisional and may be subject to changes

The goal of image processing class is to provide the fundamentals of developing an intelligent machine vision system. The goal is to study and analyse images and videos to understand their content and derive 3D information. Problems in this field concern the identification of 3D shapes of an acquired scene, to determine how objects move, and recognize objects through the analysis of still images or a sequence of them (ie through static and / or time-varying information). The course provides an introduction to classical image processing techniques and end up to introduce the Deep Learning methodologies that are nowadays at the basis of all the disrupting innovations in several sectors: self-driving cars, security for face recognition and behaviour understanding, precision medicine and agricolture etc

at the end of the course the student will be able to:
be familiar with the theoretical and practical aspects of image processing; have acquired the basics of the image formation process and understand the relationships between the 2D and 3D world; have acquired the essential ingredients to develop a processing pipeline to locate, recognize and track objects of interest.
Having acquired the basic principles of Deep Neural Networks (Deep Learning) and transfer learning in order to build intelligent vision systems

Introduzione ai sistemi di visione artificiale (2 ore); Formazione dell’immagine (3 ore); Geometria proiettiva 2D e 3D (3 ore); Miglioramento della qualità delle immagini (2 ore); analisi delle immagini a colori (2 ore); Filtraggio nello spazio e nel dominio delle frequenze (4 ore); Piramidi Gaussiane e Laplaciane (3 ore); Local feature detector (4 ore); Allineamento (4 ore); Segmentazione (3 ore); analisi della tessitura (2 ore); analisi del movimento (4 ore); structure from motion (2 ore); multi-view geometry (2); Riconoscimento automatico (2) Deep Learning (8 ore); Tracking (2 ore).

[1] Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010.

[2] Deep Learning, by Goodfellow, Bengio, and Courville.

[2] Dictionary of Computer Vision and Image Processing, by Fisher et al. Note: Full text is available in 'Online Resources' section.

Semester
Secondo Semestre (dal 04/03/2019 al 04/06/2019)

Exam type
Obbligatorio

Type of assessment
Orale - Voto Finale

Course timetable
https://easyroom.unisalento.it/Orario

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