Intelligent machines: an introductory perspective of artificial intelligence and robotics
Intelligent machines: an introductory perspective of artificial intelligence and robotics
Advanced animation and rendering techniques
Advanced animation and rendering techniques
State of the art in computer graphics: visualization and modeling
State of the art in computer graphics: visualization and modeling
CAD/Cam Theory and Practice
Introduction to Computer Graphics
Introduction to Computer Graphics
Understanding Artificial Intelligence
Understanding Artificial Intelligence
Interactive Display of Large NURBS Models
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
IEEE Computer Graphics and Applications
Extending Neural Networks for B-Spline Surface Reconstruction
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Applying Functional Networks to Fit Data Points from B-Spline Surfaces
CGI '01 Computer Graphics International 2001
A New Artificial Intelligence Paradigm for Computer-Aided Geometric Design
AISC '00 Revised Papers from the International Conference on Artificial Intelligence and Symbolic Computation
Generalized NURBS Curves and Surfaces
GMP '04 Proceedings of the Geometric Modeling and Processing 2004
G1 continuity conditions of adjacent NURBS surfaces
Computer Aided Geometric Design
Approximate swept volumes of NURBS surfaces or solids
Computer Aided Geometric Design
Functional networks for B-spline surface reconstruction
Future Generation Computer Systems
Information Sciences: an International Journal
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Three dimensional coordinate values of parametric NURBS (Non-Uniform Rational B-Splines) surfaces are obtained from two dimensional parameters u and v. An approach for generating surfaces produces a model by giving a fixed increase to u and v values. However, the ratio of three dimensional parameters increases and fixed increase of u and v values is not always the same. This difference of ratio costs unrequired sized breaks. In this study an artificial neural network method for simulation of a NURBS surface is proposed. Free shaped NURBS surfaces and various three dimensional object simulations with different patches can be produced using a method projected as network training with respect to coordinates which are found from interval scaled parameters. Experimental results show that this method in imaging modeled surface can be used as a simulator.