Uni-modal versus joint segmentation for region-based image fusion

JJ Lewis; SG Nikolov; CN Canagarajah; DR Bull; A Toet

doi:10.1109/ICIF.2006.301565

Uni-modal versus joint segmentation for region-based image fusion

JJ Lewis, SG Nikolov, CN Canagarajah, DR Bull, A Toet

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

23 Citations (Scopus)

534 Downloads (Pure)

Abstract

A number of segmentation techniques are compared with regard to their usefulness for region-based image and video fusion. In order to achieve this, a new multi-sensor data set is introduced containing a variety of infra-red, visible and pixel fused images together with manually produced "ground truth" segmentations. This enables the objective comparison of joint and unimodal segmentation techniques. A clear advantage to using joint segmentation over unimodal segmentation, when dealing with sets of multi-modal images, is shown. The relevance of these results to region-based image fusion is confirmed with task-based analysis and a quantitative comparison of the fused images produced using the various segmentation algorithms

Translated title of the contribution	Uni-modal versus joint segmentation for region-based image fusion
Original language	English
Title of host publication	9th International Conference on Information Fusion, 2006 (ICIF '06) Florence, Italy
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1 - 8
Number of pages	8
ISBN (Print)	0972184465, 1424409535
DOIs	https://doi.org/10.1109/ICIF.2006.301565
Publication status	Published - Jul 2006
Event	9th International Conference on Information Fusion - Florence, Italy Duration: 1 Jul 2006 → …

Conference

Conference	9th International Conference on Information Fusion
Country/Territory	Italy
City	Florence
Period	1/07/06 → …

Bibliographical note

Rose publication type: Conference contribution

Sponsorship: This work has been partially funded by the UK MOD
Data and Information Fusion Defence Technology Centre.
The original “UN Camp”, “Trees”, “Dune” and
“Sea” IR and visible images are kindly supplied by
TNO Human Factors Research Institute and the Octec
images by David Dwyer of Octec Ltd. These images
are available online at ImageFusion.org. The “Face”
images are taken from the Human Identification at a
Distance data set, produced by Equinox Corp. available
at equinoxsensors.com.

Terms of use: Copyright © 2006 IEEE. Reprinted from 9th International Conference on Information Fusion, 2006 (ICIP2006).

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected].

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Keywords

multi-modal segmentation
evaluation of segmentation
region-based
image fusion
human segmentation

Access to Document

10.1109/ICIF.2006.301565

Lewis IEEE ICIF 2006Accepted author manuscript, 5.11 MB

http://hdl.handle.net/1983/1399

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Cite this

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abstract = "A number of segmentation techniques are compared with regard to their usefulness for region-based image and video fusion. In order to achieve this, a new multi-sensor data set is introduced containing a variety of infra-red, visible and pixel fused images together with manually produced {"}ground truth{"} segmentations. This enables the objective comparison of joint and unimodal segmentation techniques. A clear advantage to using joint segmentation over unimodal segmentation, when dealing with sets of multi-modal images, is shown. The relevance of these results to region-based image fusion is confirmed with task-based analysis and a quantitative comparison of the fused images produced using the various segmentation algorithms",

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note = "Rose publication type: Conference contribution Sponsorship: This work has been partially funded by the UK MOD Data and Information Fusion Defence Technology Centre. The original “UN Camp”, “Trees”, “Dune” and “Sea” IR and visible images are kindly supplied by TNO Human Factors Research Institute and the Octec images by David Dwyer of Octec Ltd. These images are available online at ImageFusion.org. The “Face” images are taken from the Human Identification at a Distance data set, produced by Equinox Corp. available at equinoxsensors.com. Terms of use: Copyright {\textcopyright} 2006 IEEE. Reprinted from 9th International Conference on Information Fusion, 2006 (ICIP2006). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. ; 9th International Conference on Information Fusion ; Conference date: 01-07-2006",

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Lewis, JJ, Nikolov, SG, Canagarajah, CN, Bull, DR & Toet, A 2006, Uni-modal versus joint segmentation for region-based image fusion. in 9th International Conference on Information Fusion, 2006 (ICIF '06) Florence, Italy. Institute of Electrical and Electronics Engineers (IEEE), pp. 1 - 8, 9th International Conference on Information Fusion, Florence, Italy, 1/07/06. https://doi.org/10.1109/ICIF.2006.301565

Uni-modal versus joint segmentation for region-based image fusion. / Lewis, JJ; Nikolov, SG; Canagarajah, CN et al.
9th International Conference on Information Fusion, 2006 (ICIF '06) Florence, Italy. Institute of Electrical and Electronics Engineers (IEEE), 2006. p. 1 - 8.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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KW - multi-modal segmentation

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KW - human segmentation

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SN - 1424409535

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BT - 9th International Conference on Information Fusion, 2006 (ICIF '06) Florence, Italy

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 9th International Conference on Information Fusion

Y2 - 1 July 2006

ER -

Uni-modal versus joint segmentation for region-based image fusion

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Bibliographical note

Keywords

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