"RIV/68407700:21230/10:00175514!RIV11-GA0-21230___" . "2"^^ . "RIV/68407700:21230/10:00175514" . . "269782" . . . . "P(7E10044), P(GAP103/10/1585), R" . . . "Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features" . . "[982C026D8B97]" . "Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features"@en . . "Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features" . . . . "2"^^ . . "Matching by Normalized Cross-Correlation-Reimplementation, Comparison to Invariant Features"@en . "Pet\u0159\u00ED\u010Dek, Tom\u00E1\u0161" . "computer vision; image matching"@en . "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." . . "Svoboda, Tom\u00E1\u0161" . . "21230" . . "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 . . .