About: Enabling Manual Intervention for Otherwise Automated Registration of Large Image Series

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Enabling Manual Intervention for Otherwise Automated Registration of Large Image Series
Creator	Grothausmann, Roman Knudsen, Lars Mccormick, Matt Mühlfeld, Christian Zukić, Dženan
topic	covid:d98d303270c2ebf6d69da545c1db107a8a1fa972#this
source	PMC
abstract	Aligning thousands of images from serial imaging techniques can be a cumbersome task. Methods ([2, 11, 21]) and programs for automation exist (e.g. [1, 4, 10]) but often need case-specific tuning of many meta-parameters (e.g. mask, pyramid-scales, denoise, transform-type, method/metric, optimizer and its parameters). Other programs, that apparently only depend on a few parameter often just hide many of the remaining ones (initialized with default values), often cannot handle challenging cases satisfactorily. Instead of spending much time on the search for suitable meta-parameters that yield a usable result for the complete image series, the described approach allows to intervene by manually aligning problematic image pairs. The manually found transform is then used by the automatic alignment as an initial transformation that is then optimized as in the pure automatic case. Therefore the manual alignment does not have to be very precise. This way the worst case time consumption is limited and can be estimated (manual alignment of the whole series) in contrast to tuning of meta-parameters of pure auto-alignment of complete series which can hardly be guessed.
has issue date	2020-05-13(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50120-4_3
has license	no-cc
sha1sum (hex)	d98d303270c2ebf6d69da545c1db107a8a1fa972
schema:url	https://doi.org/10.1007/978-3-030-50120-4_3
resource representing a document's title	Enabling Manual Intervention for Otherwise Automated Registration of Large Image Series
has PubMed Central identifier	PMC7279934
schema:publication	Biomedical Image Registration
resource representing a document's body	covid:d98d303270c2ebf6d69da545c1db107a8a1fa972#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/d98d303270c2ebf6d69da545c1db107a8a1fa972
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