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A Two Stage Neural Architecture for Segmentation and Superquadrics Recovery from Range Data

  • Antonio Chella
  • Roberto Pirrone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2486)

Abstract

A novel, two stage, neural architecture for the segmentation of range data and their modeling with undeformed superquadrics is presented. The system is composed by two distinct neural networks: a SOM is used to perform data segmentation, and, for each segment, a multilayer feed-forward network performs model estimation.

Keywords

SOM Range data segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Antonio Chella
    • 1
  • Roberto Pirrone
    • 1
  1. 1.DINFO - University of Palermo and CERE-CNRPalermoItaly

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