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Similar Tensor Arrays: A Framework for Storage of Tensor Array Data
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. (Centre for image analsis)ORCID iD: 0000-0002-4405-6888
Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, Spain. (Laboratorio de Procesado de Imagen (LPI))
(5 Electrical & Electronics Engineering Department, Boğaziçi University, Istanbul, Turkey)
(Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, Spain)
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2009 (English)In: Tensors in Image Processing and Computer Vision, London: Springer , 2009, 1, 407-428 p.Chapter in book (Other academic)
Abstract [en]

Abstract 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.

Place, publisher, year, edition, pages
London: Springer , 2009, 1. 407-428 p.
Series
Advances in Pattern Recognition, ISSN 1617-7916
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-111490DOI: 10.1007/978-1-84882-299-3_19ISBN: 978-1-84882-298-6 (print)OAI: oai:DiVA.org:uu-111490DiVA: diva2:281384
Available from: 2009-12-15 Created: 2009-12-15 Last updated: 2016-04-22Bibliographically approved

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Publisher's full texthttp://www.springerlink.com/content/w6j2825rp677j677/

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Brun, Anders

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