Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Geometry Compression for 3D Polygonal Models using a Neural Network

    154820_32133_Geometry compression for 3D polygonal models etc_pub ver.pdf (1.743Mb)
    Access Status
    Open access
    Authors
    Rumman, N.
    El-Seoud, S.
    Khatatneh, K.
    Gütl, Christian
    Date
    2010
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Rumman, Nadine Abu and El-Seoud, Samir Abou and Khatatneh, Khalaf F. and Gütl, Christian. 2010. Geometry Compression for 3D Polygonal Models using a Neural Network. International Journal of Computer Applications. 1 (29): pp. 13-22.
    Source Title
    International Journal of Computer Applications
    ISSN
    0975-8887
    School
    School of Information Systems
    Remarks

    This International Journal of Computer Applications journal article can be accessed via the Related Link.

    URI
    http://hdl.handle.net/20.500.11937/26017
    Collection
    • Curtin Research Publications
    Abstract

    Three dimensional models are commonly used in computer graphics and 3D modeling characters in animation movies and games. 3D objects are more complex to handle than other multimedia data due to the fact that various representations exist for the same object, yielding a number of difficulties, among of which are the distinct sources of 3D data. Research work in the field of three dimensional environments is represented by a broad spectrum of applications. In this paper we restrict ourselves only on how to do compression using a neural network in order to minimize the size of 3D models for making transmission over networks much faster. The main objective behind this compression is to simplify the 3D model and make handling the large size of 3d objects much easier for other processes. Even the process of rendering, digital watermarking, etc., will be faster and more efficient.

    Related items

    Showing items related by title, author, creator and subject.

    • Model based methods for locating, enhancing and recognising low resolution objects in video
      Kramer, Annika (2009)
      Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most ...
    • Effective computational models for timetabling problem
      Aizam, Nur Aidya Hanum (2013)
      Timetabling is a table of information showing when certain events are scheduled to take place. Timetabling is in fact very essential in making sure that all events occur in the time and place required. It is critical in ...
    • Professional development in HIV prevention education for teachers using flexible learning and tutor support
      Jackson, Glenda Joy (2004)
      HIV prevention programs in schools are acknowledged as one of the best prospects for controlling the world HIV epidemic. Epidemiological evidence indicates that deaths world-wide from AIDS are yet to peak. Although HIV ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.