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DC poleHodnotaJazyk
dc.contributor.authorGosseaume, Julien
dc.contributor.authorKpalma, Kidiyo
dc.contributor.authorRonsin, Joseph
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-11-08T09:02:42Z
dc.date.available2017-11-08T09:02:42Z
dc.date.issued2014
dc.identifier.citationWSCG 2014: communication papers proceedings: 22nd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 257-264.en
dc.identifier.isbn978-80-86943-71-8
dc.identifier.uriwscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26421
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2014: communication papers proceedingsen
dc.rights@ Václav Skala - UNION Agencycs
dc.subjectutajené hodnocenícs
dc.subjectutajené metrikycs
dc.subjectidentifikace objektucs
dc.subjectsegmentace objektucs
dc.subjectsaliency mapacs
dc.subjectlidský vizuální systémcs
dc.titleSAMI: SAliency based metrics of Identification for object concealment evaluationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWe propose original metrics for estimation of detection and identification of an object in an image. SAMI (SAliency based Metrics of Identification) gives a detection score, called D_score, and an identification score, called I_score, for a Region Of Interest (ROI), basically the footprint area of the object. The contribution of this paper is attractive since SAMI is basically a simple easy-to-implement heuristic method based on existing image processing techniques and some intuition-based postulates. SAMI has initially been conceived to estimate the performance of SCOTT, a “Synthesis COncealment Two-level Texture” algorithm. However, a direct derived application of such metrics could be the evaluation of saliency algorithms for object segmentation: the best saliency algorithm would be the one with the highest SAMI D_score for a given object. Another possible application could be the use of SAMI inside a saliency algorithm, to compute a dense modified saliency map, in which each pixel has the SAMI D_score corresponding to its neigborhood (used as ROI). Such a resulting map would be more robust to saliency noise from small spots.en
dc.subject.translatedconcealment evaluationen
dc.subject.translatedconcealment metricsen
dc.subject.translatedobject identificationen
dc.subject.translatedobject segmentationen
dc.subject.translatedsaliency mapen
dc.subject.translatedhuman visual systemen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2014: Communication Papers Proceedings

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