SURFCOMP
Comparing and Analyzing Collections of Surfaces
| Coordinatore | WEIZMANN INSTITUTE OF SCIENCE
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Israel [IL] |
| Totale costo | 1˙113˙744 € |
| EC contributo | 1˙113˙744 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2012-StG_20111012 |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2012 |
| Periodo (anno-mese-giorno) | 2012-09-01 - 2017-08-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
WEIZMANN INSTITUTE OF SCIENCE
Organization address
address: HERZL STREET 234 contact info |
IL (REHOVOT) | hostInstitution | 1˙113˙744.00 |
| 2 |
WEIZMANN INSTITUTE OF SCIENCE
Organization address
address: HERZL STREET 234 contact info |
IL (REHOVOT) | hostInstitution | 1˙113˙744.00 |
Mappa
Word cloud
Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.
Obiettivo del progetto (Objective)
'The proposed research program intends to cover all aspects of the problem of learning and analyzing collections of surfaces and apply the developed methods and algorithms to a wide range of scientific data.
The proposal has two parts:
In the first part of the proposal, we concentrate on developing the most basic operators comparing automatically pairs of surfaces. Although this problem has received a lot of attention in recent years, and significant progress has been made, there is still a great need for algorithms that are both efficient/tractable and come with guarantees of convergence or accuracy. The main difficulty in most approaches so far is that they work in a huge and non-linear search space to compare surfaces; most algorithms resort to gradient descent from an initial guess, risking to find only local optimal solution. We offer a few research directions to tackle this problem based on the idea of identifying EFFICIENT search spaces that APPROXIMATE the desired optimal correspondence.
In the second part of the proposal we propose to make use of the methods developed in the first part to perform global analysis of, or learn, collections of surfaces. We put special emphasis on ``real-world' applications and intend to validate our algorithm on a significant collection, including data-sets such as biological anatomic data-sets and computer graphics' benchmark collections of surfaces. We propose to formulate and construct geometric structures on these collections and investigate their domain specific implications.'