Image and volume data analysis (2019/2020)

Course code
4S001409
Credits
6
Coordinator
Umberto Castellani
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Language of instruction
Italian
Teaching is organised as follows:
Activity Credits Period Academic staff Timetable
Teoria 5 I semestre Umberto Castellani

Laboratorio 1 I semestre Umberto Castellani

Learning outcomes

The course aims to provide the knowledge necessary to understand and use algorithms to process digital images and different types of spatially related data (volumes and surfaces). Therefore, algorithms and data structures will be presented to effectively code the data, segment the regions of interest, characterize with descriptors, recognize objects and align the structures (registration). At the end of the course, the student will have to demonstrate knowledge and understanding skills that allow him to exploit data acquired by multimodal probes to perform 3D reconstruction, measurement, recognition and information fusion. In addition, the student must demonstrate that he is able to use notions of computational geometry, algebra, and algorithms on graphs to solve practical problems in various application contexts, autonomously select the most appropriate data structures and the best algorithms. The student will then be able to present an application project describing effectively motivations and choices, continuing the studies independently in the Visual Computing domain.

Syllabus

Introduction
Elements of differential geometry
3D data representation,
Acquisition: 3D scanning techniques,
Registration: Iterative Closest Point and its variants,
Integration of polygonal mesh: marching cube,
Differential properties on discrete mesh: normals, curvatures, laplacian,
3D data processing: smooting and decimation,
Geodesic distances,
Spectral shape analysis,
From rigid word to morphable models,
Rigging and skinning,
Applications

Assessment methods and criteria

The exam can be obtained with three different options:
A) Oral with discussion on lab exercise (max 28/30, average between the two modalities).
B) Project with discussion on lab exercise (max 28/30, average between the two modalities).
C) Oral+Project (average between the two modalities).
Oral is a discussion on the program. The aim is to verify the knowledge of theoretical and practical aspects of involved topics.
The discussion of lab exercise consists of the delivering of an archive with the scripts that implement the algorithms described in the program. The discussion aims at verifying the correct practical implementation of the theoretical aspects addressed during the course.
The project is focused on a specific and innovative topic that is identified with the teacher. The topic can be an open issue of the state of the art or a specific applicative theme. The student will be able to generalize the knowledge acquired during the course for the solution of new problems.

 Activity Author Title Publisher Year ISBN Note Teoria Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez, Bruno Levy Polygon mesh processing A K Peters/CRC Press 2010 9781568814261