Title:	PixLabelCV - Labeling images for semantic segmentation fast, pixel-precise and offline
Authors:	Schraml, Dominik Trambickii, Konstantin Notni, Gunther
Citation:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 47-56
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/57376
ISSN:	2464–4625 (online) 2464–4617 (print)
Keywords:	sémantická segmentace;nástroje pro anotaci obrázků;pixelově přesné značení;algoritmy počítačového vidění;anotační software
Keywords in different language:	semantic segmentation;image annotation tools;pixel-precise labeling;computer vision algorithms;annotation software
Abstract in different language:	Image 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2024: Full Papers Proceedings

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