Patch-cuts: A graph-based image segmentation method using patch features and spatial relations

Gerd Brunner, Deepak R. Chittajallu, Uday Kurkure, Ioannis A. Kakadiaris

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

In this paper, we present a graph-based image segmentation method (patch-cuts) that incorporates features and spatial relations obtained from image patches. In the first step, patch-cuts extracts a set of patches that can assume arbitrary shape and size. Patches are determined by a combination of intensity quantization and morphological operations and render the proposed method robust against noise. Upon patch extraction, a set of intensity, texture and shape features are computed for each patch. These features are integrated and minimized simultaneously in a tunable energy function. Patch-cuts explores the benefit of information theory-based measures such as the Kullback-Leibler and the Jensen-Shannon divergence in its energy terms. In our experiments, we applied patchcuts to general images as well as to non-contrast Computed Tomography heart scans.

Original languageEnglish (US)
DOIs
StatePublished - 2010
Event2010 21st British Machine Vision Conference, BMVC 2010 - Aberystwyth, United Kingdom
Duration: Aug 31 2010Sep 3 2010

Conference

Conference2010 21st British Machine Vision Conference, BMVC 2010
CountryUnited Kingdom
CityAberystwyth
Period8/31/109/3/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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