The application of DBF neural networks for object recognition

  • Authors:
  • Wenming Cao;Feng Hao;Shoujue Wang

  • Affiliations:
  • Institute of Intelligent Information System, Information College, Zhejiang University of Technology, Hangzhou 310032, China;Institute of Intelligent Information System, Information College, Zhejiang University of Technology, Hangzhou 310032, China;Institute of Semiconductors, Chinese Academy of Science, Beijing 100083, China

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal
  • Year:
  • 2004

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Abstract

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively.