First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Human and Social Sciences and Cultural Heritage
STRATEGIES IN COMMUNICATION.
Course unit
AUGMENTED AND VIRTUAL REALITY
SUP9087218, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
STRATEGIES IN COMMUNICATION.
IF0315, Degree course structure A.Y. 2015/16, A.Y. 2019/20
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination AUGMENTED AND VIRTUAL REALITY
Department of reference Department of Linguistic and Literary Studies
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge SIMONE MILANI ING-INF/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines L-ART/06 Cinema, Photography and Television 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 42 108.0 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2015 course timetable

Examination board
Board From To Members of the board
1 1920 01/10/2019 30/11/2020 MILANI SIMONE (Presidente)
BADIA LEONARDO (Membro Effettivo)
ERSEGHE TOMASO (Supplente)

Syllabus
Prerequisites: Basic computer knowledge is required.

Some preliminary knowledge on computer graphics software can be useful (although not strictly necessary).
Target skills and knowledge: The course will give a clear sense for the deep interconnections between computer vision, computer graphics, and machine learning which lies behind virtual and augmented reality applications.
Moreover, it will allow to understand how such technologies can be applied to different fields of communications, rehabilitation, education and training.
Lectures are structured in order to provide a non-expert student with a good knowledge about basic principles and possible applications.

In its various articulations the course is expected to provide the following knowledge and skills:
1. To be aware of the main computer vision principles and strategies, as well as of their possible applications.
2. To learn the main computer graphics solutions and their employment in the creation of human-computer interfaces.
3. To know the fundamental elements of machine learning and the main algorithms of artificial intelligence, with a specific focus on neural networks.
4. To learn the main characteristics of AR and VR applications, as well as their applications to different fields of communication.
7. To develop some practical skills in implementing basic AR/VR applications.

Students will also have the opportunity to develop and test some of the proposed solutions by means of lab sessions.
Examination methods: Final evaluation will be performed by means of a written exam and a final project to be presented in the final exam. The final score will be made of a weighted average of the evaluation of the written exam (60%) and the final project (40%).

The evaluation topics for the written exam will be clearly indicated during the course and in the course material.
Assessment criteria: The final evaluation will be determined on the knowledge level of the students regarding the topics presented during the course and his/her ability to analyze the application of some overviewed strategies to communication problems. Such topics will be clearly indicated during the course and in the course material.

Evaluation criteria will be:
1. Completeness of the acquired knowledge within the fields of computer vision, 3D graphics and machine learning.
2. Ability to analyze an augmented/virtual reality application and identify the possible technical solutions.
3. Property in the technical terminology used, both written and oral.

Every efforts on the student part revealing personal involvement and special care will be recognized in terms of scores.
Course unit contents: 1) Human perception model: visual and aural perception. Subjective quality and Quality-of-Experience.

2) Usability of a AR/VR application. Human-Computer Interfaces (HCIs). Gestual interfaces. Speech interfaces. Principles of data visualization.

3) Image formation and camera model. Fish-eye and 360 cameras.
3D reconstruction systems: passive and active methods. Stereopsys amd multi-camera systems, Structure-from-Motion (SfM) algorithms, depth-from-focus and defocus. Depth sensors and laser scanners.

4) Machine learning and neural networks. Introduction to machine learning and main applications.Neural networks: basic principles and their use in the creation of human-computer interfaces (HCI).
Artificial intelligence strategies applied to computer vision.

5) Visualization and graphical rendering. 3D displays, VR visors, and augmented reality devices. Rendering principles.

6) Augmented and virtual reality.
Applications in medical fields and rehabilitation.
Applications in cultural heritage and preservation.
Training and educational applications.
Advertisement and journalism.
Planned learning activities and teaching methods: The course offers a guided tour of the computer vision, machine learning, and computer graphics topics needed for current virtual and augmented reality applications.

The course rationale can be divided into three main parts:
a) description and modelling of imaging systems for images and 3D models acquisition;
b) classification and deep learning strategies for AR applications;
c) rendering real or virtual 3D models to standard images and 3D/AR devices.

Part a) has the objective of explaining the operation and the mathematical models of current imaging systems (e.g., video-cameras, Time of Flight systems like MS Kinect, and many more) in the language of computational photography. These systems will be analyzed focusing on their capability of reconstructing 3D models of static and dynamic scenes using standard images (with special focus on stereo and active stereo systems) and/or depth cameras. Part b) will also deal with the problem of image classification and scene understanding by means of machine learning algorithms. The objective of Part (c) is to introduce the rendering methods and their adaptation to the specific viewing devices. In this final part, the course will also introduce the problem of the interaction between real and virtual world (mixed reality, human-computer interfaces).


The topics are treated by means of frontal lectures with computational examples based on MATLAB, Open CV and Unity.
The appraisal is stimulated by lab sessions and a final project confronting the student with practical situations due to the concepts seen in class.

The theoretical part of the course will be presented in 19 frontal lectures, while 5 laboratory lectures will be given to guide students across the programming of augmented reality applications.
Additional notes about suggested reading: The study material is given by the class-notes made available before every class meeting.
The notes distill and condense various research papers and content coming from several textbooks.

The frontal teaching activities involve the use of transparencies, blackboard skecthes, as well as example programs to be tested at home. All the teaching material presented during the lectures is made available on the platform "http://elearning.dei.unipd.it". Students will be provided with a MATLAB license by the University of Padova for lab exercise and programming. A student license for the platform Unity will be available for free download as well.
Textbooks (and optional supplementary readings)
  • Klette, Reinhard, Concise Computer Vision. Springer London: --, 2014. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Problem solving
  • Auto correcting quizzes or tests for periodic feedback or exams
  • Active quizzes for Concept Verification Tests and class discussions
  • Video shooting made by the teacher/the students
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)
  • Unity

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Kaltura (desktop video shooting, file loading on MyMedia Unipd)
  • Matlab

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education Decent Work and Economic Growth Industry, Innovation and Infrastructure Sustainable Cities and Communities