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dc.contributor.authorAkçay, H. Gökhanen_US
dc.contributor.authorAksoy, Selimen_US
dc.coverage.spatialBeijing, China
dc.date.accessioned2018-04-12T11:49:27Z
dc.date.available2018-04-12T11:49:27Z
dc.date.issued2016-07en_US
dc.identifier.urihttp://hdl.handle.net/11693/37732
dc.descriptionDate of Conference: 10-15 July 2016
dc.descriptionConference name: IEEE International Geoscience and Remote Sensing Symposium, (IGARSS) 2016
dc.description.abstractDetection of compound structures that are comprised of different arrangements of simpler primitive objects has been a challenging problem as commonly used bag-of-words models are limited in capturing spatial information. We have developed a generic method that considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and detects new instances of compound structures through automatic selection of subsets of candidate regions from a hierarchical segmentation by maximizing the likelihood of their individual appearances and relative spatial arrangements. In this paper, we extend the model to handle different types of primitive objects that come from multiple hierarchical segmentations. Results are shown for the detection of different types of housing estates in a WorldView-2 image. © 2016 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.titleInternational Geoscience and Remote Sensing Symposium, (IGARSS) 2016en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IGARSS.2016.7730328en_US
dc.subjectContextual modelingen_US
dc.subjectMarkov random fielden_US
dc.subjectObject detectionen_US
dc.subjectSpatial relationshipsen_US
dc.subjectCompounds
dc.subjectBuildings
dc.subjectImage edge detection
dc.subjectMarkov processes
dc.subjectImage segmentation
dc.subjectRandom variables
dc.subjectContext modeling
dc.titleDetection of compound structures by region group selection from hierarchical segmentationsen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage5095en_US
dc.citation.epage5098en_US
dc.identifier.doi10.1109/IGARSS.2016.7730328en_US
dc.publisherIEEEen_US


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