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Conference Paper

Medial Features for Superpixel Segmentation

MPS-Authors
/persons/resource/persons83902

Engel,  D
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83871

Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognitive Engineering, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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http://www.mva-org.jp/mva2009/
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MVA-2009-Engel.pdf
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Citation

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 Conference Committee.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C4F4-3
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.