This HTML5 document contains 46 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n11http://localhost/temp/predkladatel/
n5http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n22http://linked.opendata.cz/ontology/domain/vavai/riv/uzemniOchranaPatentu/
n14http://linked.opendata.cz/resource/domain/vavai/subjekt/
n12http://linked.opendata.cz/ontology/domain/vavai/
n21http://linked.opendata.cz/resource/domain/vavai/zamer/
n16http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F12%3A00198523%21RIV13-MSM-21230___/
rdfshttp://www.w3.org/2000/01/rdf-schema#
skoshttp://www.w3.org/2004/02/skos/core#
n19http://linked.opendata.cz/ontology/domain/vavai/riv/vyuzitiPatentuVzoru/
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/licencniPoplatek/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n23http://linked.opendata.cz/ontology/domain/vavai/riv/vyuzitiJinymSubjektem/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n20http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n8http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00198523%21RIV13-MSM-21230___
rdf:type
n12:Vysledek skos:Concept
rdfs:seeAlso
https://register.epo.org/espacenet/application?number=EP06797568
dcterms:description
An object of the present invention is to provide an image search method that is robust and fast (with computational complexity of logarithmic order relative to the number of models). An image search method of the present invention comprises a normalization step of extracting a plurality of regions from one or more model images and normalizing the regions as standard regions; a classification step of setting a specific region in each normalized standard region and classifying the plurality of standard regions under two or more subsets on the basis of a feature of the specific region; a recursive classification step of iteratively performing an operation of setting another specific region at a location different from that of the aforementioned specific region in each standard region classified in each subset and classifying the plurality of standard regions under still more subsets on the basis of a feature of the other specific region; and an output step of outputting the locations of the specific regions in the standard regions in the respective classifications and the features of the specific regions in the classifications. An object of the present invention is to provide an image search method that is robust and fast (with computational complexity of logarithmic order relative to the number of models). An image search method of the present invention comprises a normalization step of extracting a plurality of regions from one or more model images and normalizing the regions as standard regions; a classification step of setting a specific region in each normalized standard region and classifying the plurality of standard regions under two or more subsets on the basis of a feature of the specific region; a recursive classification step of iteratively performing an operation of setting another specific region at a location different from that of the aforementioned specific region in each standard region classified in each subset and classifying the plurality of standard regions under still more subsets on the basis of a feature of the other specific region; and an output step of outputting the locations of the specific regions in the standard regions in the respective classifications and the features of the specific regions in the classifications.
dcterms:title
IMAGE SEARCH METHOD AND DEVICE IMAGE SEARCH METHOD AND DEVICE
skos:prefLabel
IMAGE SEARCH METHOD AND DEVICE IMAGE SEARCH METHOD AND DEVICE
skos:notation
RIV/68407700:21230/12:00198523!RIV13-MSM-21230___
n12:predkladatel
n14:orjk%3A21230
n3:aktivita
n13:Z
n3:aktivity
Z(MSM6840770038)
n3:cisloPatentuVzoru
EP1930852
n3:datumUdeleniPatentuVzoru
2012-07-25+02:00
n3:dodaniDat
n8:2013
n3:domaciTvurceVysledku
n5:1711326 n5:2734621
n3:druhVysledku
n18:P
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
140499
n3:idVysledku
RIV/68407700:21230/12:00198523
n3:jazykVysledku
n20:eng
n3:klicovaSlova
image search; image retrieval; affine-invariant frames
n3:klicoveSlovo
n7:image%20search n7:image%20retrieval n7:affine-invariant%20frames
n3:kontrolniKodProRIV
[A1A9D0F043BD]
n3:licencniPoplatek
n17:A
n3:mistoVydaniPatentuVzoru
Munich, The Hague, Berlin, Vienna, Brussels
n3:nazevVydavatelePatentuVzoru
Evropský patentový úřad
n3:obor
n6:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n8:2012
n3:tvurceVysledku
Obdržálek, Štěpán Matas, Jiří Sakai, K.
n3:uzemniOchranaPatentu
n22:A
n3:vlastnik
n16:vlastnikVysledku
n3:vyuzitiJinymSubjektem
n23:A
n3:vyuzitiPatentuVzoru
n19:A
n3:zamer
n21:MSM6840770038
n3:vlastnikPatentuVzoru
ČVUT Praha 50% Toyota Jidosha Kabushiki Kaisha 50%
n11:organizacniJednotka
21230