A Graph-based Framework for Sub-pixel Image Segmentation
2011 (English)In: Theoretical Computer Science, ISSN 0304-3975, Vol. 412, no 15, 1338-1349 p.Article in journal (Refereed) Published
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 fuzzysegmented graphs. Interpreting the edges as one-dimensional paths betweenthe 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.
Place, publisher, year, edition, pages
2011. Vol. 412, no 15, 1338-1349 p.
Image segmentation, Graph labeling, Graph cuts, Coverage segmentation, Sub-pixel segmentation, Feature estimation
Computer Vision and Robotics (Autonomous Systems)
Research subject Computerized Image Analysis; Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-149256DOI: 10.1016/j.tcs.2010.11.030ISI: 000288420900005OAI: oai:DiVA.org:uu-149256DiVA: diva2:404323