Image and volume data analysis (2016/2017)

Course code
Name of lecturer
Andrea Giachetti
Andrea Giachetti
Number of ECTS credits allocated
Academic sector
Language of instruction
I sem. dal Oct 3, 2016 al Jan 31, 2017.

Lesson timetable

I sem.
Day Time Type Place Note
Tuesday 2:30 PM - 3:30 PM laboratorio Laboratory Gamma from Nov 8, 2016  to Jan 31, 2017
Tuesday 3:30 PM - 5:30 PM laboratorio Laboratory Gamma  
Wednesday 1:30 PM - 4:30 PM lesson Lecture Hall L from Nov 16, 2016  to Jan 31, 2017
Wednesday 3:30 PM - 6:30 PM lesson Lecture Hall B from Oct 28, 2016  to Nov 9, 2016

Learning outcomes

The goal of the course is to give the student to the ability to understand and the basic methods to encode and process digital images as well as any kind of spatially referenced data (volumes, surfaces).
Different data structures and algorithms will be presented allowing smart encoding of relevant information, segmentation of the regions of interest from sampled data, characterization of objects geometry, aligning different acquisitions.

At the end of the course, the student will be able to use a variety of Computational Geometry tools and other algorithms in order to solve different problems in different applicative contexts and to have the possibility of continuing his specialization in multimedia with increased autonomy.


1 Introduction
Spatially referenced data in 2D and 3D, scattered data, gridded data, sensors
2 Data structures
Images and volumes, binary and label data, topology. Scattered data, triangulation and interpolation
Surface and contour representations: curve approximations, splines; point clouds, meshes, meshing algorithms Moving Least Squares, ball pivoting.
3 Segmentation
Classification-based approaches, graph based approaches,
4 Contour/surface based segmentation
Snakes and active surfaces 3D, Level Sets, fast marching algorithm, distance maps and geodesic distances
5 Model based segmentation ,
Model fitting Hough transform, statistical modelling with training data: Point Distribution Models
6 Shape analysis
Moments, binary region descriptors, invariance properties.
Contour based descriptors, local features, signatures, Fourier descriptors, context in local descriptors.
Skeletons and medial lines,
Differential geometry of surfaces, descriptors on meshes
7 Image and texture features
8 Spatial registration
Problem descriptions and approaches. Landmarks based registration,
Intensity based registration

Laboratorio Matlab
Introduzione a Matlab, images, contours, sampling and interpolation, splines
Voxel based segmentation
2D Snake implementation
2D Shape retrieval
Mesh processing, 3D shape retrieval

Assessment methods and criteria

Written test (20/30) and evaluation of programming skills (10/30)

To pass the exam, the student must show
- they have understood the principles related to surface and volume Data encoding and processing
- they are able to describe these concepts in a clear and exhaustive way
- they are able to apply the acquired knowledge in different applicative contexts

Written test:
The written test is composed by a few open questions and/or exercises testing the understanding of the different topics of the course.

The lab exam will consist of a small project applying methods learned in the course to application specific data