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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 6/2014

01.08.2014 | Original Paper

Graph-cut based interactive segmentation of 3D materials-science images

verfasst von: Jarrell Waggoner, Youjie Zhou, Jeff Simmons, Marc De Graef, Song Wang

Erschienen in: Machine Vision and Applications | Ausgabe 6/2014

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Abstract

Segmenting materials’ images is a laborious and time-consuming process, and automatic image segmentation algorithms usually contain imperfections and errors. Interactive segmentation is a growing topic in the areas of image processing and computer vision, which seeks to find a balance between fully automatic methods and fully-manual segmentation processes. By allowing minimal and simplistic interaction from the user in an otherwise automatic algorithm, interactive segmentation is able to simultaneously reduce the time taken to segment an image while achieving better segmentation results. Given the specialized structure of materials’ images and level of segmentation quality required, we show an interactive segmentation framework for materials’ images that has three key contributions: (1) a multi-labeling approach that can handle a large number of structures while still quickly and conveniently allowing manual addition and removal of segments in real-time, (2) multiple extensions to the interactive tools which increase the simplicity of the interaction, and (3) a web interface for using the interactive tools in a client/server architecture. We show a full formulation of each of these contributions and example results from their application.
Vorheriger Artikel Extrinsic calibration of heterogeneous cameras by line images

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For clarity, we inverted the image intensity in this figure, as well as several other figures in the later sections. The original is similar in appearance to Fig. 11.
 
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Metadaten
Titel
Graph-cut based interactive segmentation of 3D materials-science images
verfasst von
Jarrell Waggoner
Youjie Zhou
Jeff Simmons
Marc De Graef
Song Wang
Publikationsdatum
01.08.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 6/2014
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-014-0616-3

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