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  Medial Features for Superpixel Segmentation

Engel, D., Spinello L, Triebel R, Siegwart R, Bülthoff, H., & Curio, C. (2009). Medial Features for Superpixel Segmentation. In Eleventh IAPR Conference on Machine Vision Applications (MVA 2009) (pp. 248-252). Tokyo, Japan: MVA Organizing Committee.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C4F4-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C4FC-4
Genre: Conference Paper

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Engel, D1, Author           
Spinello L, Triebel R, Siegwart R, Bülthoff, HH1, Author           
Curio, C1, Author           
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 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.

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 Dates: 2009-05
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: URI: http://www.mva-org.jp/mva2009/
BibTex Citekey: 5760
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Title: Eleventh IAPR Conference on Machine Vision Applications (MVA 2009)
Place of Event: Yokohama, Japan
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Title: Eleventh IAPR Conference on Machine Vision Applications (MVA 2009)
Source Genre: Proceedings
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Affiliations:
Publ. Info: Tokyo, Japan : MVA Organizing Committee
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 248 - 252 Identifier: -