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Hierarchical Layered Mean Shift Methods

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8192))

Abstract

Many image processing tasks exist and segmentation is one of them. We are focused on the mean-shift segmentation method. Our goal is to improve its speed and reduce the over-segmentation problem that occurs with small spatial bandwidths. We propose new mean-shift method called Hierarchical Layered Mean Shift. It uses hierarchical preprocessing stage and stacking hierarchical segmentation outputs together to minimise the over-segmentation problem.

The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64

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Šurkala, M., Mozdřeň, K., Fusek, R., Sojka, E. (2013). Hierarchical Layered Mean Shift Methods. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_48

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  • DOI: https://doi.org/10.1007/978-3-319-02895-8_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02894-1

  • Online ISBN: 978-3-319-02895-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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