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

  • Dario Lodi Rizzini
  • Fabio Oleari
  • Andrea Atti
  • Jacopo Aleotti
  • Stefano Caselli
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


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.


Point Cloud Object Recognition Object Detection Markov Random Fields Iterative Close Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is partially supported by MARIS project.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dario Lodi Rizzini
    • 1
  • Fabio Oleari
    • 1
    • 2
  • Andrea Atti
    • 1
  • Jacopo Aleotti
    • 1
  • Stefano Caselli
    • 1
  1. 1.RIMLab-Robotics and Intelligent Machines Laboratory, Dipartimento di Ingegneria dell’InformazioneUniversity of ParmaParmaItaly
  2. 2.Elettric80 S.p.a.VianoItaly

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