Medial features for superpixel segmentation

David Engel, Luciano Spinello, Rudolph Triebel, Roland Siegwart, Heinrich Bulthoff, Cristóbal Curio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Image segmentation plays an important role in computer vision and human scene perception. Image oversegmentation is a common technique to overcome the problem of managing the high number of pixels and the reasoning among them. Specifically, a local and coherent cluster that contains a statistically homogeneous region is denoted as a superpixel. In this paper we propose a novel algorithm that segments an image into superpixels employing a new kind of shape centered feature which serve as a seed points for image segmentation, based on Gradient Vector Flow fields (GVF) [14]. The features are located at image locations with salient symmetry. We compare our algorithm to state-of-the-art superpixel algorithms and demonstrate a performance increase on the standard Berkeley Segmentation Dataset.

Original languageEnglish
Title of host publicationProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
Pages248-252
Number of pages5
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, Japan
Duration: 2009 May 202009 May 22

Other

Other11th IAPR Conference on Machine Vision Applications, MVA 2009
CountryJapan
CityYokohama
Period09/5/2009/5/22

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Engel, D., Spinello, L., Triebel, R., Siegwart, R., Bulthoff, H., & Curio, C. (2009). Medial features for superpixel segmentation. In Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009 (pp. 248-252)