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Medial Features for Superpixel Segmentation

MPG-Autoren
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Engel,  D
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Spinello L, Triebel R, Siegwart R, Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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.


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