Randomized and Deterministic Approaches to The Sparse Correspondence Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F07%3A03138374" target="_blank" >RIV/68407700:21230/07:03138374 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Randomized and Deterministic Approaches to The Sparse Correspondence Problem

Popis výsledku v původním jazyce

We summarize recent progress in the image features correspondence problem and we present a robust solution based on a radically different notion than a standard optimization or robust statistics methods. Even if the method represents measurement uncertainty explicitly, the existence of a unique solution is guaranteed. We formulate the correspondence problem as a graph theory problem of a finding stable independent vertex set (strict sub-kernel) in an oriented graph. The vertices of the graph are all possible correspondences, the edges capture the structure of the constraints and edge orientation represents pairwise comparison ''is better'' based on correspondence quality, including the uncertainty of this comparison. Our method allows encoding both non-parametric and parametric constraints with unknown parameters to the graph.

Název v anglickém jazyce

Randomized and Deterministic Approaches to The Sparse Correspondence Problem

Popis výsledku anglicky

We summarize recent progress in the image features correspondence problem and we present a robust solution based on a radically different notion than a standard optimization or robust statistics methods. Even if the method represents measurement uncertainty explicitly, the existence of a unique solution is guaranteed. We formulate the correspondence problem as a graph theory problem of a finding stable independent vertex set (strict sub-kernel) in an oriented graph. The vertices of the graph are all possible correspondences, the edges capture the structure of the constraints and edge orientation represents pairwise comparison ''is better'' based on correspondence quality, including the uncertainty of this comparison. Our method allows encoding both non-parametric and parametric constraints with unknown parameters to the graph.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2007

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů