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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Chella, A., Pirrone, R. (2002). A Two Stage Neural Architecture for Segmentation and Superquadrics Recovery from Range Data. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_14
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DOI: https://doi.org/10.1007/3-540-45808-5_14
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