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DC poleHodnotaJazyk
dc.contributor.authorSchraml, Dominik
dc.contributor.authorTrambickii, Konstantin
dc.contributor.authorNotni, Gunther
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-25T19:31:15Z-
dc.date.available2024-07-25T19:31:15Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 47-56en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57376
dc.format10 scs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectsémantická segmentacecs
dc.subjectnástroje pro anotaci obrázkůcs
dc.subjectpixelově přesné značenícs
dc.subjectalgoritmy počítačového viděnícs
dc.subjectanotační softwarecs
dc.titlePixLabelCV - Labeling images for semantic segmentation fast, pixel-precise and offlineen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedImage annotation, also called labeling is a necessary task for any supervised learning approach to obtain ground truth data for model training. This article offers a comprehensive survey of contemporary image annotation tools, grouping freely accessible ones based on their service range, speed, and data privacy assurances. In our exploration for tools capable of executing pixel-precise semantic labeling, we identified a shortage of swift, free image annotation tools that don’t require users to upload their data to third-party servers. Therefore, we introduce "PixLabelCV" - a lightweight, fast, offline, and standalone annotation tool primarily developed to aid human annotators in achieving pixel-perfect labels promptly. Uniquely crafted to be freely available (open source) and non-server-based, it ensures enhanced privacy and efficiency. Hence, it is aimed to serve as an ideal tool to facilitate labeling data for smaller labs and businesses. At its core, PixLabelCV fuses conventional labeling techniques such as delineating objects with rectangles or polygons with multiple computer vision algorithms. Spanning basic thresholding in RGB or HSV color space to more intricate procedures like flood fill or watershed the tool instantaneously computes and exhibits the resulting segmentations. Annotators can swiftly add these segments to a class label or refine them by adjusting parameters or markers before a quick repetition. To further augment the user experience, additional functionalities like mor phological closing are incorporated, facilitating an intuitive labeling process. Another standout feature is its ability to uniquely assign pixels to singular classes, eliminating any potential overlap-induced ambiguities.en
dc.subject.translatedsemantic segmentationen
dc.subject.translatedimage annotation toolsen
dc.subject.translatedpixel-precise labelingen
dc.subject.translatedcomputer vision algorithmsen
dc.subject.translatedannotation softwareen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.6
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:WSCG 2024: Full Papers Proceedings

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