Abstract
This paper aims at improving the well-known local variance segmentation method by adding extra signal modi and specific processing steps. As a key contribution, we extend the uni-modal segmentation method to perform multi-modal analysis, such that any number of signal modi available can be incorporated in a very flexible way. We have found that the use of a combined weight of luminance and depth values improves the segmentation score by 6.8%, for a large and challenging multi-modal dataset. Furthermore, we have developed an improved uni-modal texture-segmentation algorithm. This improvement relies on a clever choice of the color space and additional pre- and post-processing steps, by which we have increased the segmentation score on a challenging texture dataset by 2.1%. This gain is mainly preserved when using a different dataset with worse lighting conditions and different scene types.
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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Sanberg, W.P., Do, L., de With, P.H.N. (2013). Flexible Multi-modal Graph-Based Segmentation. 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_44
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DOI: https://doi.org/10.1007/978-3-319-02895-8_44
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