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Theoretical foundation of the intersecting cortical model and its use for change detection of aircraft, cars, and nuclear explosion tests
KTH, Superseded Departments, Physics.
2004 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 84, no 7, 1131-1146 p.Article in journal (Refereed) Published
Abstract [en]

The intersecting cortical model (ICM) is a model based on neural network techniques especially designed for image processing. It was derived from several visual cortex models and is basically the intersection of these models, i.e. the common elements amongst these models. The theoretical foundation of the ICM is given and it is shown how the ICM can be derived as a reduced set of equations of the pulse-coupled neural network based upon models proposed by Eckhorn and Reitboeck. Tests of the ICM are presented: one on a series of images of an aircraft moving in the sky; two on car detection; and one on preparations of underground nuclear explosions. The ICM is shown here, in a few examples, to be useful in imagery change detection: aircraft moving against a homogeneous background without precise geometric matching; car on a road; two cars moving in an urban setting without precise geometric matching; and for a linear structure in a complex background. The ICM can be used when the moving objects are not too small and the background is not too difficult. Changes involving larger linear structures can be detected even if the background is not homogeneous.

Place, publisher, year, edition, pages
2004. Vol. 84, no 7, 1131-1146 p.
Keyword [en]
ICM, pulse-coupled neural network, visual cortex, change detection, motion detection, level set methods, curvature flow, diffusion, autowaves, satellite images, PNE
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-41257DOI: 10.1016/j.sigpro.2004.03.012ISI: 000222149800006Scopus ID: 2-s2.0-2642550949OAI: oai:DiVA.org:kth-41257DiVA: diva2:443300
Note
QC 20110923Available from: 2011-09-23 Created: 2011-09-23 Last updated: 2017-12-08Bibliographically approved

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