DOICV

Discrete Optimization in Computer Vision: Theory and Practice

 Coordinatore Institute of Science and Technology Austria 

Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.

 Nazionalità Coordinatore Austria [AT]
 Totale costo 1˙641˙585 €
 EC contributo 1˙641˙585 €
 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-2013-CoG
 Funding Scheme ERC-CG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-06-01   -   2019-05-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    Institute of Science and Technology Austria

 Organization address address: Am Campus 1
city: Klosterneuburg
postcode: 3400

contact info
Titolo: Dr.
Nome: Vladimir
Cognome: Kolmogorov
Email: send email
Telefono: +43 224390004801
Fax: 43224400000000

AT (Klosterneuburg) hostInstitution 1˙641˙585.00
2    Institute of Science and Technology Austria

 Organization address address: Am Campus 1
city: Klosterneuburg
postcode: 3400

contact info
Titolo: Ms.
Nome: Carla
Cognome: Mazuheli-Chibidziura
Email: send email
Telefono: +43 224390001038
Fax: +43 224390002000

AT (Klosterneuburg) hostInstitution 1˙641˙585.00

Mappa


 Word cloud

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

sequence    model    potts    map    classes    computer    estimation    inference    graphical    cuts    techniques    discrete       tagging    graph    vision    tools    algorithms    optimization    recently    functions    submodular    models    energies    oriented   

 Obiettivo del progetto (Objective)

'This proposal aims at developing new inference algorithms for graphical models with discrete variables, with a focus on the MAP estimation task. MAP estimation algorithms such as graph cuts have transformed computer vision in the last decade; they are now routinely used and are also utilized in commercial systems. Topics of this project fall into 3 categories. Theoretically-oriented: Graph cut techniques come from combinatorial optimization. They can minimize a certain class of functions, namely submodular functions with unary and pairwise terms. Larger classes of functions can be minimized in polynomial time. A complete characterization of such classes has been established. They include k-submodular functions for an integer k _ 1. I investigate whether such tools from discrete optimization can lead to more efficient inference algorithms for practical problems. I have already found an important application of k-submodular functions for minimizing Potts energy functions that are frequently used in computer vision. The concept of submodularity also recently appeared in the context of the task of computing marginals in graphical models, here discrete optimization tools could be used. Practically-oriented: Modern techniques such as graph cuts and tree-reweighted message passing give excellent results for some graphical models such as with the Potts energies. However, they fail for more complicated models. I aim to develop new tools for tackling such hard energies. This will include exploring tighter convex relaxations of the problem. Applications, sequence tagging problems: Recently, we developed new algorithms for inference in pattern-based Conditional Random Fields (CRFs) on a chain. This model can naturally be applied to sequence tagging problems; it generalizes the popular CRF model by giving it more flexibility. I will investigate (i) applications to specific tasks, such as the protein secondary structure prediction, and (ii) ways to extend the model.'

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