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

Authors:
Antonio Chella;Roberto Pirrone
Affiliations:
-;-
Venue:
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Year:
2002

Citing 5
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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.