Paper Details: Downloads: 487
Serial Number: P1120819001
Title: Deriving Hidden Junction in Solid Model Reconstruction Using Neural Network
Authors: M. Z. Matondang, H. Haron
Abstract: Research on solid model reconstruction has been started since 1970s and it is still investigated since then. In other fields of research, it also takes important role such as in fields as diverse as product design, engineering and rapid prototyping, medical imaging and artistic applications. Furthermore, it has turned out to be essential needs. This paper presents a new algorithm in reconstructing solid model using neural network with back propagation that has been successfully used by few researchers in deriving depth values of visible junction. The algorithm presented in this paper extends previous works by deriving hidden junction and produces complete solid model. Affine transformation in form of rotation is employed to generate the coordinate values that are required in the development of the proposed algorithm. Besides the algorithm, this paper also presents a new framework in solid model reconstruction using neural network. Comparison among three algorithms of previous works in
Keywords: Hidden junction, Solid model reconstruction Neural Network, Back propagation.
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 8
Issue: 2
Submission Date: 5/6/2008 12:00:00 AM
Review Date: 6/21/2008 12:00:00 AM
Publishing Date: 8/14/2008 12:00:00 AM
Article Downloads: 487
Download:

Facebook