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Proceedings Paper

Interactive grain image segmentation using graph cut algorithms
Author(s): Jarrell Waggoner; Youjie Zhou; Jeff Simmons; Ayman Salem; Marc De Graef; Song Wang
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Paper 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 and 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 two key contributions: 1) a multi-labeling framework that can handle a large number of structures while still quickly and conveniently allowing manual interaction in real-time, and 2) a parameter estimation approach that prevents the user from having to manually specify parameters, increasing the simplicity of the interaction. We show a full formulation of each of these contributions and example results from their application.

Paper Details

Date Published: 14 February 2013
PDF: 12 pages
Proc. SPIE 8657, Computational Imaging XI, 86570I (14 February 2013); doi: 10.1117/12.2014161
Show Author Affiliations
Jarrell Waggoner, Univ. of South Carolina (United States)
Youjie Zhou, Univ. of South Carolina (United States)
Jeff Simmons, Air Force Research Lab. (United States)
Ayman Salem, Materials Resources, LLC (United States)
Marc De Graef, Carnegie Mellon Univ. (United States)
Song Wang, Univ. of South Carolina (United States)


Published in SPIE Proceedings Vol. 8657:
Computational Imaging XI
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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