Tomography Defined as Sensor Fusion

K.B. Ozanyan

doi:10.1109/ICSENS.2015.7370554

Tomography Defined as Sensor Fusion

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Tomography is introduced as a sensing task, ratherthan a healthcare imaging application, best known for itsembodiment in hospital scanners. Tomography sensing, imagingand data processing are summarized with emphasis on systemphenomenology and design in relation with the inversion ofmeasurement data. The main classification paradigms of sensorfusion are examined and the overlap with the theory and practiceof computer (‘hard-field’) tomography is shown in detail. It isdemonstrated how concurrent fusion takes place when measuringpath integrals; collaborative fusion – when defining a projectionas a set of path integrals to allow data inversion from multipleprojections, e.g. through the Fourier slice theorem;complementary fusion – when imaging the object in several autoregisteredsecondary contrasts. To cover the largest varietyavailable, process tomography (i.e. non-medical tomography)case studies are examined in detail revealing the fusion tasksperformed by various algorithms.

Original language	English
Title of host publication	Proceedings IEEE SENSORS 2015
Place of Publication	New Jersey, USA
Publisher	IEEE
DOIs	https://doi.org/10.1109/ICSENS.2015.7370554
Publication status	Published - 1 Nov 2015
Event	IEEE SENSORS 2015 - Busan, South Korea Duration: 2 Nov 2015 → 4 Nov 2015

Conference

Conference	IEEE SENSORS 2015
City	Busan, South Korea
Period	2/11/15 → 4/11/15

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@inproceedings{b27707ccc0d34acea46cc403c6935052,

title = "Tomography Defined as Sensor Fusion",

abstract = "Tomography is introduced as a sensing task, ratherthan a healthcare imaging application, best known for itsembodiment in hospital scanners. Tomography sensing, imagingand data processing are summarized with emphasis on systemphenomenology and design in relation with the inversion ofmeasurement data. The main classification paradigms of sensorfusion are examined and the overlap with the theory and practiceof computer ({\textquoteleft}hard-field{\textquoteright}) tomography is shown in detail. It isdemonstrated how concurrent fusion takes place when measuringpath integrals; collaborative fusion – when defining a projectionas a set of path integrals to allow data inversion from multipleprojections, e.g. through the Fourier slice theorem;complementary fusion – when imaging the object in several autoregisteredsecondary contrasts. To cover the largest varietyavailable, process tomography (i.e. non-medical tomography)case studies are examined in detail revealing the fusion tasksperformed by various algorithms.",

author = "K.B. Ozanyan",

year = "2015",

month = nov,

day = "1",

doi = "10.1109/ICSENS.2015.7370554",

language = "English",

booktitle = "Proceedings IEEE SENSORS 2015",

publisher = "IEEE",

address = "United States",

note = "IEEE SENSORS 2015 ; Conference date: 02-11-2015 Through 04-11-2015",

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T1 - Tomography Defined as Sensor Fusion

AU - Ozanyan, K.B.

PY - 2015/11/1

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N2 - Tomography is introduced as a sensing task, ratherthan a healthcare imaging application, best known for itsembodiment in hospital scanners. Tomography sensing, imagingand data processing are summarized with emphasis on systemphenomenology and design in relation with the inversion ofmeasurement data. The main classification paradigms of sensorfusion are examined and the overlap with the theory and practiceof computer (‘hard-field’) tomography is shown in detail. It isdemonstrated how concurrent fusion takes place when measuringpath integrals; collaborative fusion – when defining a projectionas a set of path integrals to allow data inversion from multipleprojections, e.g. through the Fourier slice theorem;complementary fusion – when imaging the object in several autoregisteredsecondary contrasts. To cover the largest varietyavailable, process tomography (i.e. non-medical tomography)case studies are examined in detail revealing the fusion tasksperformed by various algorithms.

AB - Tomography is introduced as a sensing task, ratherthan a healthcare imaging application, best known for itsembodiment in hospital scanners. Tomography sensing, imagingand data processing are summarized with emphasis on systemphenomenology and design in relation with the inversion ofmeasurement data. The main classification paradigms of sensorfusion are examined and the overlap with the theory and practiceof computer (‘hard-field’) tomography is shown in detail. It isdemonstrated how concurrent fusion takes place when measuringpath integrals; collaborative fusion – when defining a projectionas a set of path integrals to allow data inversion from multipleprojections, e.g. through the Fourier slice theorem;complementary fusion – when imaging the object in several autoregisteredsecondary contrasts. To cover the largest varietyavailable, process tomography (i.e. non-medical tomography)case studies are examined in detail revealing the fusion tasksperformed by various algorithms.

U2 - 10.1109/ICSENS.2015.7370554

DO - 10.1109/ICSENS.2015.7370554

M3 - Conference contribution

BT - Proceedings IEEE SENSORS 2015

PB - IEEE

CY - New Jersey, USA

T2 - IEEE SENSORS 2015

Y2 - 2 November 2015 through 4 November 2015

ER -

Tomography Defined as Sensor Fusion

Abstract

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