Visualizing the Uncertainty of Graph‐based 2D Segmentation with Min‐path Stability

This paper presents a novel approach to visualize the uncertainty in graph‐based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solu...

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Vydáno v:	Computer graphics forum Ročník 36; číslo 3; s. 133 - 143
Hlavní autoři:	Summa, B., Tierny, J., Pascucci, V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Blackwell Publishing Ltd 01.06.2017 Wiley
Témata:	Categories and Subject Descriptors (according to ACM CCS) Complexity Computational Geometry Computer graphics methodology, Image processing segmentation, Pixel classification Computer Science Discrete Mathematics I.3.6 [Computer Graphics]: Methodology and Techniques—Graphics data structures and data types I.4.6 [Image Processing and Computer Vision]: Segmentation—Pixel classification MATHEMATICS AND COMPUTING Minima Segmentation Stability analysis Uncertainty Wire
ISSN:	0167-7055, 1467-8659
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Popis
Shrnutí:	This paper presents a novel approach to visualize the uncertainty in graph‐based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solution is often uncertain due to noise or blur in the underlying data as well as imprecision in user input. Our approach provides insight into this uncertainty by computing the “min‐path stability”, a scalar measure analyzing the stability of the segmentation given a set of input constraints. Our approach is efficient, easy to compute, and can be generally applied to either graph cuts or live‐wire (even partial) segmentations. In addition to its general applicability, our new approach to graph cuts uncertainty visualization improves on the time complexity of the current state‐of‐the‐art with an additional fast approximate solution. We also introduce a novel query enabled by our approach which provides users with alternate segmentations by efficiently extracting local minima of the segmentation optimization. Finally, we evaluate our approach and demonstrate its utility on data from scientific and medical applications.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 NA0002375; SC0007446 National Science Foundation (NSF) USDOE National Nuclear Security Administration (NNSA)
ISSN:	0167-7055 1467-8659
DOI:	10.1111/cgf.13174