選択的注意モデルの学習情報の記号化によるハイブリッド画像理解の実現 Realization of Hybrid Image Understanding through the Symbolization of Learned Information in the Selective Attention Model

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Pattern processing and symbolic processing are the major methods used in an image understanding task. Conventionally, they are often implemented and handled as independent systems. However, the handling of real images requires a method that incorporates the characteristics of both. But the designing of a method that enables interaction between symbolic processing and pattern processing is not an easy task. In this paper, we propose a method for symbol-pattern mutual transformation, which, through symbolization of the connection knowledge acquired by the Selective Attention Model, lays the foundation for symbol-pattern integration. We demonstrate the model's effectiveness by applying it to an object segmentation problem.

収録刊行物

  • 日本神経回路学会誌 = The Brain & neural networks

    日本神経回路学会誌 = The Brain & neural networks 11(2), 47-55, 2004-06-05

    日本神経回路学会

参考文献:  15件中 1-15件 を表示

被引用文献:  1件中 1-1件 を表示

各種コード

  • NII論文ID(NAID)
    10013361433
  • NII書誌ID(NCID)
    AA11658570
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    1340766X
  • データ提供元
    CJP書誌  CJP引用  J-STAGE 
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