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dc.contributor.authorHenry, Christopher J.
dc.contributor.authorRamanna, Sheela
dc.contributor.authorLevy, Daniel
dc.date.accessioned2019-08-27T21:43:39Z
dc.date.available2019-08-27T21:43:39Z
dc.date.issued2013
dc.identifier.citationHenry, Christopher J., Sheela Ramanna, and Daniel Levy. "Quantifying nearness in visual spaces." Cybernetics and Systems 44(1) (2013): 38-56. DOI: 10.1080/01969722.2012.732797.en_US
dc.identifier.issn0196-9722
dc.identifier.urihttp://hdl.handle.net/10680/1732
dc.descriptionPreprint versionen_US
dc.description.abstractCybernetic vision systems can be deployed in problem domains where the goal is to achieve results similar to those produced by humans. Fundamentally, these problems consist of evaluation of image content between sets of images. This article contrasts two theoretical frameworks for image comparison, namely, the semantic similarity approach used in the earth mover's distance (EMD) and the integrated region matching (IRM) similarity measure, with the tolerance nearness measure (tNM) based on near set theory. The contribution of this article is a comparison of the image similarity measures EMD, IRM, and tNM, as well as a signature-based approach to calculating the tolerance nearness measure.en_US
dc.description.sponsorship"This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) grant 194376, the University of Winnipeg Research Start-Up Grant, and the University of Winnipeg Major Research Grant."en_US
dc.description.urihttps://www.tandfonline.com/doi/abs/10.1080/01969722.2012.732797en_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEarth mover's distanceen_US
dc.subjectIntegrated region matchingen_US
dc.subjectTolerance nearness measureen_US
dc.subjectTolerance spaceen_US
dc.subjectVision systemen_US
dc.titleQuantifying nearness in visual spacesen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/01969722.2012.732797en_US


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