About: Efficient Sequential Correspondence Selection by Cosegmentation

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.176
Description	Object recognition and wide baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that (i) has high precision (ii) has good recall and (iii) is fast. The sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. The statistics are projected on a prominent discriminative direction by SVM. Wald's sequential probability ratio test is performed on the SVM projection computed on progressively larger cosegmented regions.We show experimentally that the proposed Sequential Correspondence Verification (SCV) algorithm significantly outperforms the correspondence selection method based on SIFT distance ratios. Object recognition and wide baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process coupled with a quasi-optimal sequential decision process leads to a correspondence verification procedure that (i) has high precision (ii) has good recall and (iii) is fast. The sequential decision on the correctness of a correspondence is based on simple statistics of a modified dense stereo matching algorithm. The statistics are projected on a prominent discriminative direction by SVM. Wald's sequential probability ratio test is performed on the SVM projection computed on progressively larger cosegmented regions.We show experimentally that the proposed Sequential Correspondence Verification (SCV) algorithm significantly outperforms the correspondence selection method based on SIFT distance ratios. (en)
Title	Efficient Sequential Correspondence Selection by Cosegmentation Efficient Sequential Correspondence Selection by Cosegmentation (en)
skos:prefLabel	Efficient Sequential Correspondence Selection by Cosegmentation Efficient Sequential Correspondence Selection by Cosegmentation (en)
skos:notation	RIV/68407700:21230/10:00168986!RIV14-MSM-21230___
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(1ET101210406), P(7E08031), Z(MSM6840770038)
http://linked.open...iv/cisloPeriodika	9
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Matas, Jiří Čech, Jan Perďoch, Michal
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	256404
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/10:00168986
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	correspondence; verification; dense stereo; growing; sequential decison; learning; SVM; wide-baseline stereo; image retrieval; SIFT; RANSAC (en)
http://linked.open.../riv/klicoveSlovo	RANSAC SVM correspondence growing dense stereo sequential decison wide-baseline stereo learning verification SIFT image retrieval
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[EFE1F455910D]
http://linked.open...i/riv/nazevZdroje	IEEE Transactions on Pattern Analysis and Machine Intelligence
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Automatic 3D Virtual Model Builder from Photographs Dynamic Interactive Perception-action Learning in Cognitive Systems
http://linked.open...UplatneniVysledku	2010
http://linked.open...v/svazekPeriodika	32
http://linked.open...iv/tvurceVysledku	Matas, Jiří Čech, Jan Perďoch, Michal
http://linked.open...ain/vavai/riv/wos	000279969000003
http://linked.open...n/vavai/riv/zamer	Decision Making and Control for Manufacturing III
issn	0162-8828
number of pages	14 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/TPAMI.2009.176
http://localhost/t...ganizacniJednotka	21230

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