• DocumentCode
    2658131
  • Title

    An object recognition system using self-organising neural networks

  • Author

    Chandrasekaran, V. ; Palaniswami, M. ; Caelli, Terry

  • Author_Institution
    Sch. of Inf. Technol. & Electr. Eng., Melbourne Univ., Parkville, Vic., Australia
  • fYear
    1991
  • fDate
    18-21 Nov 1991
  • Firstpage
    2582
  • Abstract
    An object recognition system is proposed using a self-organizing neural network as a basic module for the processing of feature vectors to provide evidence for the recognition state. The modules are integrated to represent various instances of the object scene for which the features are known a priori. The basic architecture of the system proposed was configured to accept a single feature vector or multiple feature vectors at a time. The system was trained on a hypothetical three-object data set for recognition capabilities on object scenes with and without occlusion. The simulation results confirmed the success of the proposed approach
  • Keywords
    computerised pattern recognition; computerised picture processing; neural nets; self-adjusting systems; multiple feature vectors; object recognition system; occlusion; self-organising neural networks; single feature vector; three-object data set; Artificial neural networks; Australia; Data mining; Feature extraction; Humans; Information technology; Layout; Neural networks; Object recognition; Pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991. 1991 IEEE International Joint Conference on
  • Print_ISBN
    0-7803-0227-3
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.170778
  • Filename
    170778