dc.creatorERIKA DANAE LOPEZ ESPINOZA
dc.creatorLEOPOLDO ALTAMIRANO ROBLES
dc.date2010
dc.date.accessioned2022-10-12T20:04:01Z
dc.date.available2022-10-12T20:04:01Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1383
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4128015
dc.descriptionIn Markov random field (MRF) models, parameters such as internal and external reference fields are used. In this paper, the influence of these parameters in the segmentation quality is analyzed, and it is shown that, for image segmentation, a MRF model with a priori energy function defined by means of non-homogeneous internal and external field has better segmentation quality than a MRF model defined only by a homogeneous internal reference field. An analysis of the MRF models in terms of segmentation quality, computational time and tests of statistical significance is done. Significance tests showed that the segmentations obtained with MRF model defined by means of non-homogeneous reference fields are significant at levels of 85% and 75%.
dc.formatapplication/pdf
dc.languageeng
dc.publisherJournal of Applied Research and Technology
dc.relationcitation:López-Espinoza, E.D. & Altamirano-Robles, L. (2010). Reference fields analysis of a Markov random field model to improve image segmentation, Journal of Applied Research and Technology, Vol. 8 (2): 260-273
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Image segmentation/Image segmentation
dc.subjectinfo:eu-repo/classification/Unsupervised segmentation/Unsupervised segmentation
dc.subjectinfo:eu-repo/classification/Markov random field/Markov random field
dc.subjectinfo:eu-repo/classification/Non-homogeneous random field/Non-homogeneous random field
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleReference fields analysis of a Markov random field model to improve image segmentation
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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