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A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation

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dc.contributor.author Cayllahua-Cahuina E. es_PE
dc.contributor.author Cousty J. es_PE
dc.contributor.author Guimarães S. es_PE
dc.contributor.author Kenmochi Y. es_PE
dc.contributor.author Cámara-Chávez G. es_PE
dc.contributor.author de Albuquerque Araújo A. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2019
dc.description The research leading to these results has received funding from the French Agence Nationale de la Recherche, grant ANR-15-CE40-0006 (CoMeDiC), the Brazilian Federal Agency of Support and Evaluation of Postgraduate Education (program CAPES/PVE: grant 064965/2014-01), Brazilian Federal Agency of Research (CNPq/Universal 421521/2016-3 and CNPq/PQ 307062/2016-3), Fundo de Amparo Pesquisa do Estado de Minas Gerais (FAPEMIG/PPM 00006-16), the Peruvian agency Consejo Nacional de Ciencia, Tecnológica CONCYTEC (contract N 101-2016-. FONDECYT-DE). The first author would like to thank Brazilian agencies CNPq and CAPES and Peruvian agency CONCYTEC for the financial support during his thesis.
dc.description.abstract Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/978-3-030-14085-4_14
dc.identifier.isbn 9783030140847
dc.identifier.scopus 2-s2.0-85064214726
dc.identifier.uri https://hdl.handle.net/20.500.12390/503
dc.language.iso eng
dc.publisher Springer Verlag
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Segmentation results
dc.subject Geometry es_PE
dc.subject Graphic methods es_PE
dc.subject Dissimilarity measures es_PE
dc.subject Hierarchical graphs es_PE
dc.subject Hierarchical segmentation es_PE
dc.subject Scale selection es_PE
dc.subject Scale spaces es_PE
dc.subject Image segmentation es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#2.02.00
dc.title A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication
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