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Flexible Multi-modal Graph-Based Segmentation

  • Willem P. Sanberg
  • Luat Do
  • Peter H. N. de With
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

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.

Keywords

Multi-modal Signal Analysis RGBD Segmentation Graphs 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Willem P. Sanberg
    • 1
  • Luat Do
    • 1
  • Peter H. N. de With
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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