Detail publikace

Building Triangle Strips Using Hopfield Neural Network

Originální název

Building Triangle Strips Using Hopfield Neural Network

Anglický název

Building Triangle Strips Using Hopfield Neural Network

Jazyk

en

Originální abstrakt

The most common way to visualize three dimensional data in graphics systems is to use triangles. Firstly, the data is converted to a set of triangles. Each time the visualization is accomplished this set is sent to graphics hardware repeatedly. The speed of final visualization is limited by the rate at which the data is sent to hardware. The responsiveness of interactive applications such as virtual reality, games, etc. is highly sensitive to visualization speed. Therefore, it is crucial to optimize visualized data to reduce rendering time. One of the way to optimize rendered data supported by current graphics pipelines is to use triangle strips. Constructing optimal set of triangle strips is NP-complete problem. This paper deals with constructing such primitives using Hopfield neural network.

Anglický abstrakt

The most common way to visualize three dimensional data in graphics systems is to use triangles. Firstly, the data is converted to a set of triangles. Each time the visualization is accomplished this set is sent to graphics hardware repeatedly. The speed of final visualization is limited by the rate at which the data is sent to hardware. The responsiveness of interactive applications such as virtual reality, games, etc. is highly sensitive to visualization speed. Therefore, it is crucial to optimize visualized data to reduce rendering time. One of the way to optimize rendered data supported by current graphics pipelines is to use triangle strips. Constructing optimal set of triangle strips is NP-complete problem. This paper deals with constructing such primitives using Hopfield neural network.

BibTex


@inproceedings{BUT21439,
  author="Dominik {Pospíšil} and František {Zbořil}",
  title="Building Triangle Strips Using Hopfield Neural Network",
  annote="The most common way to visualize three dimensional data in graphics systems is to use triangles. Firstly, the data is converted to a set of triangles. Each time the visualization is accomplished this set is sent to graphics hardware repeatedly. The speed of final visualization is limited by the rate at which the data is sent to hardware. The responsiveness of interactive applications such as virtual reality, games, etc. is highly sensitive to visualization speed. Therefore, it is crucial to optimize visualized data to reduce rendering time. One of the way to optimize rendered data supported by current graphics pipelines is to use triangle strips. Constructing optimal set of triangle strips is NP-complete problem. This paper deals with constructing such primitives using Hopfield neural network.",
  address="Faculty of Electrical Engineering and Informatics, University of Technology Košice",
  booktitle="Proceedings of the Sixth International Scientific Conference ECI 2004",
  chapter="21439",
  institution="Faculty of Electrical Engineering and Informatics, University of Technology Košice",
  year="2004",
  month="september",
  pages="394--398",
  publisher="Faculty of Electrical Engineering and Informatics, University of Technology Košice",
  type="conference paper"
}