Visual Surveillance of Objects Motion Using GNG
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
A growing self-organizing network for reconstructing curves and surfaces
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Geometric bone modeling: from macro to micro structures
Journal of Computer Science and Technology
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
Fast image representation with GPU-based growing neural gas
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Video and image processing with self-organizing neural networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A growing neural gas algorithm with applications in hand modelling and tracking
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A study of a soft computing based method for 3D scenario reconstruction
Applied Soft Computing
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
Improving 3D keypoint detection from noisy data using growing neural gas
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Hi-index | 0.00 |
The neural network method, a relatively new method in reverse engineering (RE), has the potential to reconstruct 3D models accurately and fast. A neural network (NN) is a set of interconnected neurons, in which each neuron is capable of making autonomous arithmetic and geometric calculations. Moreover, each neuron is affected by its surrounding neurons through the structure of the network. This work proposes a new approach that utilizes growing neural gas neural network (GNG NN) techniques to reconstruct a triangular manifold mesh. This method has the advantage of reconstructing the surface of an n-genus freeform object without a priori knowledge regarding the original object, its topology or its shape. The resulting mesh can be improved by extending the MGNG into an adaptive algorithm. The proposed method was also extended for micro-structure modeling. The feasibility of the proposed method is demonstrated on several examples of freeform objects with complex topologies.