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Unsupervised Range Image Segmentation and Object Recognition Using Feature Proximity and Markov Random Field

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Intelligent Autonomous Systems 13

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

In this paper, we propose a framework for unsupervised range image segmentation and object recognition that exploits feature similarity and proximity as leading criteria in the processing steps. Feature vectors are distinctive traits like color, texture and shape of the regions of the scene; proximity of similar features enforces classification and association decisions. Segmentation is performed by dividing the input point cloud into voxels, by extracting and clustering features from each voxel, and by refining such segmentation through Markov Random Field model. Candidate objects are selected from the resulting regions of interest and compared with the models contained in a dataset. Object recognition is performed by aligning the models with the refined point cloud clusters. Experiments show the consistency of the segmentation algorithm as well as the potential for recognition even when partial views of the object are available.

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Acknowledgments

This research is partially supported by MARIS project.

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Authors and Affiliations

  1. RIMLab-Robotics and Intelligent Machines Laboratory, Dipartimento di Ingegneria dell’Informazione, University of Parma, Parma, Italy

    Dario Lodi Rizzini, Fabio Oleari, Andrea Atti, Jacopo Aleotti & Stefano Caselli

  2. Elettric80 S.p.a., Viano, Italy

    Fabio Oleari

Authors
  1. Dario Lodi Rizzini
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  2. Fabio Oleari
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  3. Andrea Atti
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  4. Jacopo Aleotti
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  5. Stefano Caselli
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Corresponding author

Correspondence to Dario Lodi Rizzini .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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Lodi Rizzini, D., Oleari, F., Atti, A., Aleotti, J., Caselli, S. (2016). Unsupervised Range Image Segmentation and Object Recognition Using Feature Proximity and Markov Random Field. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_58

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

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