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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F05%3A03114548%21RIV06-GA0-21230___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
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 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 Není k dispozici
dcterms:title
Sub-linear Indexing for Large Scale Object Recognition Sub-linear Indexing for Large Scale Object Recognition Není k dispozici
skos:prefLabel
Sub-linear Indexing for Large Scale Object Recognition Není k dispozici Sub-linear Indexing for Large Scale Object Recognition
skos:notation
RIV/68407700:21230/05:03114548!RIV06-GA0-21230___
n4:strany
1 ; 10
n4:aktivita
n5:P
n4:aktivity
P(GA102/03/0440)
n4:dodaniDat
n11:2006
n4:domaciTvurceVysledku
n6:2734621 n6:1711326
n4:druhVysledku
n10:D
n4:duvernostUdaju
n19:S
n4:entitaPredkladatele
n21:predkladatel
n4:idSjednocenehoVysledku
545351
n4:idVysledku
RIV/68407700:21230/05:03114548
n4:jazykVysledku
n9:eng
n4:klicovaSlova
LAF; MSER; Object recognition; local affine frames
n4:klicoveSlovo
n12:local%20affine%20frames n12:MSER n12:LAF n12:Object%20recognition
n4:kontrolniKodProRIV
[3BCB9CF2FD85]
n4:mistoKonaniAkce
Oxford
n4:mistoVydani
London
n4:nazevZdroje
BMVC 2005: Proceedings of the 16th British Machine Vision Conference
n4:obor
n13:JD
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n20:GA102%2F03%2F0440
n4:rokUplatneniVysledku
n11:2005
n4:tvurceVysledku
Obdržálek, Štěpán Matas, Jiří
n4:typAkce
n18:WRD
n4:zahajeniAkce
2005-09-05+02:00
s:numberOfPages
10
n14:hasPublisher
British Machine Vision Association
n8:isbn
1-901725-29-4
n17:organizacniJednotka
21230