Maximal Max-tree Simplification

dc.creatorSouza R.
dc.creatorRittner L.
dc.creatorMachado R.
dc.creatorLotufo R.
dc.date2014
dc.date2015-06-25T17:50:58Z
dc.date2015-11-26T15:38:44Z
dc.date2015-06-25T17:50:58Z
dc.date2015-11-26T15:38:44Z
dc.date.accessioned2018-03-28T22:47:15Z
dc.date.available2018-03-28T22:47:15Z
dc.identifier9781479952083
dc.identifierProceedings - International Conference On Pattern Recognition. Institute Of Electrical And Electronics Engineers Inc., v. , n. , p. 3132 - 3137, 2014.
dc.identifier10514651
dc.identifier10.1109/ICPR.2014.540
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84919935952&partnerID=40&md5=ab6a8d7da90032ef0f84c08876feeec8
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/85948
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/85948
dc.identifier2-s2.0-84919935952
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1263974
dc.descriptionThe Max-Tree is an efficient data structure that represents all connected components resulting from all possible image upper threshold values. Usually, most of its nodes represent irrelevant extrem a, i.e. noise, or small variations of a connected component. This paper proposes the Maximal Max-Tree Simplification (MMS) filter with a normalized threshold criterion (MMS-T) and a Maximally Stable Extremal Regions (MSER) criterion (MMS-MSER) and a methodology to apply them using the Extinction filter We show that after applying our simplification methodology which sets the number of maxima in the image, the number of Max-Tree nodes is at most twice this number. Two applications of the proposed methodology are illustrated.
dc.description
dc.description
dc.description3132
dc.description3137
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dc.languageen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationProceedings - International Conference on Pattern Recognition
dc.rightsfechado
dc.sourceScopus
dc.titleMaximal Max-tree Simplification
dc.typeActas de congresos


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