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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00175514" target="_blank" >RIV/68407700:21230/10:00175514 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features
Original language 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.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>R - Projekt Ramcoveho programu EK

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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