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Fast Graph-Based Object Segmentation for RGB-D Images

  • Giorgio Toscana
  • Stefano RosaEmail author
  • Basilio Bona
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph-based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with \(\mathcal{O}(N log N)\) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods.

Keywords

Image segmentation Robotics Depth smoothing 

Notes

Acknowledgments

This work was done in collaboration with TIM S.p.A.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.DAUINPolitecnico di TorinoTurinItaly

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