About: Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The normalized cross-correlation is one of the most popular methods for image matching. While fast implementations of the algorithm are available in standard mathematical toolboxes, there still are ways to get significant speed-up for many practical applications. This work investigates the following possibilities: reusing image sums for matching multiple templates, using maximum expected disparity to bound search regions, and using downscaling factor to reduce size of computation. Based on our experiments we conclude that both downscaling images and bounding disparity field yields significant speed-up. Downscaling images also yields higher repeatability rate, which remains reasonably high for downscaling factors up to 5. For images related by translation, matching by normalized cross-correlation gives higher repatability rate and matching score than invariant features with SIFT decriptors. The normalized cross-correlation is one of the most popular methods for image matching. While fast implementations of the algorithm are available in standard mathematical toolboxes, there still are ways to get significant speed-up for many practical applications. This work investigates the following possibilities: reusing image sums for matching multiple templates, using maximum expected disparity to bound search regions, and using downscaling factor to reduce size of computation. Based on our experiments we conclude that both downscaling images and bounding disparity field yields significant speed-up. Downscaling images also yields higher repeatability rate, which remains reasonably high for downscaling factors up to 5. For images related by translation, matching by normalized cross-correlation gives higher repatability rate and matching score than invariant features with SIFT decriptors. (en)
Title	Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features (en)
skos:prefLabel	Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features (en)
skos:notation	RIV/68407700:21230/10:00175514!RIV11-GA0-21230___
http://linked.open...avai/riv/aktivita	P R
http://linked.open...avai/riv/aktivity	P(7E10044), P(GAP103/10/1585), R
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Petříček, Tomáš Svoboda, Tomáš
http://linked.open.../riv/druhVysledku	O - Ostatní výsledky nezařaditelné do žádného z výše uvedených druhů výsledku
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	269782
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/10:00175514
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	computer vision; image matching (en)
http://linked.open.../riv/klicoveSlovo	image matching computer vision
http://linked.open...ontrolniKodProRIV	[982C026D8B97]
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Advanced predictors for object detection and tracking in video Natural human-robot cooperation in dynamic environments
http://linked.open...UplatneniVysledku	2010
http://linked.open...iv/tvurceVysledku	Petříček, Tomáš Svoboda, Tomáš
http://localhost/t...ganizacniJednotka	21230

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