SURFCOMP

Comparing and Analyzing Collections of Surfaces

Coordinatore	WEIZMANN INSTITUTE OF SCIENCE    more 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

#	participant	country	role	EC contrib. [€]
1	WEIZMANN INSTITUTE OF SCIENCE  Organization address address: HERZL STREET 234 city: REHOVOT postcode: 7610001 contact info Titolo: Dr. Nome: Yaron Cognome: Lipman Email: send email Telefono: 97289344910 Fax: 97289346023	IL (REHOVOT)	hostInstitution	1˙113˙744.00
2	WEIZMANN INSTITUTE OF SCIENCE  Organization address address: HERZL STREET 234 city: REHOVOT postcode: 7610001 contact info Titolo: Ms. Nome: Gabi Cognome: Bernstein Email: send email Telefono: +972 8 934 6728 Fax: +972 8 934 4165	IL (REHOVOT)	hostInstitution	1˙113˙744.00

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 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.'