A graph-based framework for sub-pixel image segmentation

Many image segmentation methods utilize graph structures for representing images, where the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object representation, i.e., allowing partial belongingness of elements to image objects, the unavoidable loss of informati...

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Bibliographic Details
Published in:Theoretical computer science 2011-03, Vol.412 (15), p.1338-1349
Main Authors: Malmberg, F., Lindblad, J., Sladoje, N., Nyström, I.
Format: Article
Language:English
Subjects:
Bildanalys
Computer Science
Computerized Image Analysis
Computerized Image Processing
Coverage segmentation
Datavetenskap (datalogi)
Datoriserad bildanalys
Datoriserad bildbehandling
Feature estimation
Fuzzy
Fuzzy logic
Fuzzy set theory
Graph cuts
Graph labeling
Graphs
Image analysis
Image segmentation
Information technology
Informationsteknik
Representations
Segmentation
Sub-pixel segmentation
TECHNOLOGY
TEKNIKVETENSKAP
Two dimensional
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Summary:Many image segmentation methods utilize graph structures for representing images, where the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object representation, i.e., allowing partial belongingness of elements to image objects, the unavoidable loss of information when representing continuous structures by finite sets is significantly reduced, enabling feature estimates with sub-pixel precision. This work presents a framework for object representation based on fuzzy segmented graphs. Interpreting the edges as one-dimensional paths between the vertices of a graph, we extend the notion of a graph cut to that of a located cut, i.e., a cut with sub-edge precision. We describe a method for computing a located cut from a fuzzy segmentation of graph vertices. Further, the notion of vertex coverage segmentation is proposed as a graph theoretic equivalent to pixel coverage segmentations and a method for computing such a segmentation from a located cut is given. Utilizing the proposed framework, we demonstrate improved precision of area measurements of synthetic two-dimensional objects. We emphasize that although the experiments presented here are performed on two-dimensional images, the proposed framework is defined for general graphs and thus applicable to images of any dimension.
ISSN:0304-3975
1879-2294
1879-2294
DOI:10.1016/j.tcs.2010.11.030