Combined morphological-spectral unsupervised image segmentation

RJ O'Callaghan; DR Bull

doi:10.1109/TIP.2004.838695

Combined morphological-spectral unsupervised image segmentation

RJ O'Callaghan
, DR Bull

Research output: Contribution to journal › Article (Academic Journal) › peer-review

149 Citations (Scopus)

Abstract

Probably the best unsupervised segmentation algorithm. Used as a basis for mutimodality segmentation and fusion in work funded by DIF DTC. Evaluated in trial airborne platform by QinetiQ, producing superior results to all other state of the art algorithms tried. Selected for pull through into Phase II of DIF DTC in the Multidimensional Fusion Cluster Project lead by General Dynamics. Acknowledged to be the foundation stone of our successful groundbreaking work in region based image and video fusion. Used as basis for work in the successful Link Autoarch project on archiving and metadata extraction for Wildlife archives.

Translated title of the contribution	Combined morphological-spectral unsupervised image segmentation
Original language	English
Article number	Issue 1
Pages (from-to)	49 - 62
Number of pages	14
Journal	IEEE Transactions on Image Processing
Volume	14 (1)
DOIs	https://doi.org/10.1109/TIP.2004.838695
Publication status	Published - Jan 2005

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Publisher: Institute of Electrical and Electronics Engineers (IEEE)

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10.1109/TIP.2004.838695

http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=1369329&isnumber=29968&punumber=83&k2dockey=1369329@ieeejrns&query=%28+%28+bull%3Cin%3Eau+%29+%29+%3Cand%3E+%28pyr+%3E%3D+2001+%3Cand%3E+pyr+%3C%3D+2006%29&pos=22

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title = "Combined morphological-spectral unsupervised image segmentation",

abstract = "Probably the best unsupervised segmentation algorithm. Used as a basis for mutimodality segmentation and fusion in work funded by DIF DTC. Evaluated in trial airborne platform by QinetiQ, producing superior results to all other state of the art algorithms tried. Selected for pull through into Phase II of DIF DTC in the Multidimensional Fusion Cluster Project lead by General Dynamics. Acknowledged to be the foundation stone of our successful groundbreaking work in region based image and video fusion. Used as basis for work in the successful Link Autoarch project on archiving and metadata extraction for Wildlife archives.",

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note = "Publisher: Institute of Electrical and Electronics Engineers (IEEE)",

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language = "English",

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AU - Bull, DR

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AB - Probably the best unsupervised segmentation algorithm. Used as a basis for mutimodality segmentation and fusion in work funded by DIF DTC. Evaluated in trial airborne platform by QinetiQ, producing superior results to all other state of the art algorithms tried. Selected for pull through into Phase II of DIF DTC in the Multidimensional Fusion Cluster Project lead by General Dynamics. Acknowledged to be the foundation stone of our successful groundbreaking work in region based image and video fusion. Used as basis for work in the successful Link Autoarch project on archiving and metadata extraction for Wildlife archives.

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DO - 10.1109/TIP.2004.838695

M3 - Article (Academic Journal)

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EP - 62

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JF - IEEE Transactions on Image Processing

M1 - Issue 1

ER -

Combined morphological-spectral unsupervised image segmentation

Abstract

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