Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Similar Tensor Arrays - A Framework for Storage of Tensor Array Data
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Centre for Image Analysis, SLU, Uppsala, Sweden.
Universidad de Valladolid Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática Spain.
Boğaziçi University 5 Electrical & Electronics Engineering Department Istanbul Turkey.
Universidad de Valladolid Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática Spain.
Vise andre og tillknytning
2009 (engelsk)Inngår i: Tensors in Image Processing and Computer Vision / [ed] Santiago Aja-Fern´andez, Rodrigo de Luis Garc´ıa, Dacheng Tao, Xuelong Li, Springer Science+Business Media B.V., 2009, 1, s. 407-428Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

This chapter describes a framework for storage of tensor array data, useful to describe regularly sampled tensor fields. The main component of the framework, called Similar Tensor Array Core (STAC), is the result of a collaboration between research groups within the SIMILAR network of excellence. It aims to capture the essence of regularly sampled tensor fields using a minimal set of attributes and can therefore be used as a “greatest common divisor” and interface between tensor array processing algorithms. This is potentially useful in applied fields like medical image analysis, in particular in Diffusion Tensor MRI, where misinterpretation of tensor array data is a common source of errors. By promoting a strictly geometric perspective on tensor arrays, with a close resemblance to the terminology used in differential geometry, (STAC) removes ambiguities and guides the user to define all necessary information. In contrast to existing tensor array file formats, it is minimalistic and based on an intrinsic and geometric interpretation of the array itself, without references to other coordinate systems.

sted, utgiver, år, opplag, sider
Springer Science+Business Media B.V., 2009, 1. s. 407-428
Serie
Advances in Pattern Recognition, ISSN 1617-7916
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-58091DOI: 10.1007/978-1-84882-299-3_19ISBN: 978-1-84882-298-6 (tryckt)ISBN: 978-1-84882-299-3 (tryckt)OAI: oai:DiVA.org:liu-58091DiVA, id: diva2:331961
Konferanse
Tensor in Image Processing and Computer Vision
Tilgjengelig fra: 2010-07-29 Laget: 2010-07-29 Sist oppdatert: 2018-01-12bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstfind book at a swedish library/hitta boken i ett svenskt bibliotek

Søk i DiVA

Av forfatter/redaktør
Brun, AndersSigfridsson, AndreasSvensson, BjörnHerberthson, MagnusKnutsson, Hans
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 735 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf