• DocumentCode
    2443732
  • Title

    Segmentation and classification of domestic moving objects using a syntactic approach

  • Author

    Lim, Gek ; Alder, Michael D. ; Desilva, Christopher J S

  • Author_Institution
    Centre for Intelligent Inf. Process. Syst., Western Australia Univ., Nedlands, WA, Australia
  • Volume
    7
  • fYear
    1994
  • fDate
    27 Jun-2 Jul 1994
  • Firstpage
    4259
  • Abstract
    In this paper, we propose a method for segmenting and classifying domestic moving objects using a syntactic approach. The problem is to recognise moving objects in front of the camera in a domestic environment such as human beings, curtains blown by the wind and external events such as tree branches, and the aim is to distinguish moving human heads from moving curtains or tree branches. In real-world images, the situation where a human being is moving in the foreground and at the same time the curtains and/or tree branches are moving in the background, often arises. We use quadratic neurons (represented by quadratic forms) as our simple pattern primitives and extract the structural information based on the relationships between the forms. We call this the UpWrite process, and it can be applied any number of times. To achieve our aim of recognising moving heads, we require two levels of the UpWrite process
  • Keywords
    feature extraction; image classification; image segmentation; neural nets; object recognition; UpWrite process; domestic moving object recognition; image classification; image segmentation; pattern primitives; quadratic neural networks; real-world images; structural information extraction; syntactic approach; Australia; Cameras; Data mining; Dictionaries; Head; Humans; Image segmentation; Information processing; Intelligent systems; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1901-X
  • Type

    conf

  • DOI
    10.1109/ICNN.1994.374950
  • Filename
    374950