Comparing and Analyzing Collections of Surfaces


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 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


# participant  country  role  EC contrib. [€] 

 Organization address address: HERZL STREET 234
postcode: 7610001

contact info
Titolo: Dr.
Nome: Yaron
Cognome: Lipman
Email: send email
Telefono: 97289344910
Fax: 97289346023

IL (REHOVOT) hostInstitution 1˙113˙744.00

 Organization address address: HERZL STREET 234
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


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

first    optimal    surfaces    we    problem    algorithms    data    collections    search    efficient    significant   

 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.'

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