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Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F05%3A03114548" target="_blank" >RIV/68407700:21230/05:03114548 - 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

Sub-linear Indexing for Large Scale Object Recognition

Popis výsledku v původním jazyce

Realistic approaches to large scale object recognition, i.e. for detection and localisation of hundreds or more objects, must support sub-linear time indexing. In the paper, we propose a method capable of recognising one of N objects in log(N) time. The.visual memory. is organised as a binary decision tree that is built to minimise average time to decision. Leaves of the tree represent a few local image areas, and each non-terminal node is associated with a .weak classifier.. In the recognition phase,a single invariant measurement decides in which subtree a corresponding image area is sought. The method preserves all the strengths of local affine region methods . robustness to background clutter, occlusion, and large changes of viewpoints. Experimentally we show that it supports near real-time recognition of hundreds of objects with state-of-the-art recognition rates. After the test image is processed (in a second on a current PCs), the recognition via indexing into the visual memory

Název v anglickém jazyce

Sub-linear Indexing for Large Scale Object Recognition

Popis výsledku anglicky

Realistic approaches to large scale object recognition, i.e. for detection and localisation of hundreds or more objects, must support sub-linear time indexing. In the paper, we propose a method capable of recognising one of N objects in log(N) time. The.visual memory. is organised as a binary decision tree that is built to minimise average time to decision. Leaves of the tree represent a few local image areas, and each non-terminal node is associated with a .weak classifier.. In the recognition phase,a single invariant measurement decides in which subtree a corresponding image area is sought. The method preserves all the strengths of local affine region methods . robustness to background clutter, occlusion, and large changes of viewpoints. Experimentally we show that it supports near real-time recognition of hundreds of objects with state-of-the-art recognition rates. After the test image is processed (in a second on a current PCs), the recognition via indexing into the visual memory

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA102%2F03%2F0440" target="_blank" >GA102/03/0440: Rozpoznávání lidských aktivit pro automatické sledování z videa</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2005

Kód důvěrnosti údajů

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

Název statě ve sborníku

BMVC 2005: Proceedings of the 16th British Machine Vision Conference

ISBN

1-901725-29-4

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

British Machine Vision Association

Místo vydání

London

Místo konání akce

Oxford

Datum konání akce

5. 9. 2005

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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