In this paper, a cascade fuzzy ART (CFART) network is developed and applied to 3D object recognition. The proposed CFART network contains multiple layers which can express a hierarchical representation of an input pattern. The learning processes of the proposed network are unsupervised and self-organizing, which include a top-down searching process and a bottom-up learning process. The proposed CFART can accept both binary and analog inputs. With fast learning and categorization capabilities, the proposed network is capable of acting as an extensible database and providing a multi-resolutional representation of 3D objects. In the experiments, we use superquadrics as a demonstrating example.
|Number of pages||6|
|State||Published - 1996|
|Event||Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA|
Duration: 3 Jun 1996 → 6 Jun 1996
|Conference||Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)|
|City||Washington, DC, USA|
|Period||3/06/96 → 6/06/96|