Uncertainty reduction paradigm using structural knowledge in line-drawing understanding

  • Authors:

  • Yasuo Ariki;Masashi Morimoto;Toshiyuki Sakai

  • Affiliations:

  • Department of Information Science, Faculty of Engineering, Kyoto University, Kyoto, Japan;Department of Information Science, Faculty of Engineering, Kyoto University, Kyoto, Japan;Department of Information Science, Faculty of Engineering, Kyoto University, Kyoto, Japan

  • Venue:
  • IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1987

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Abstract

In this paper, we investigate the problem of interpretation uncertainty caused by the conventional deterministic approaches to drawing image understanding from the three view points of homograph, heuristic knowledge and data ambiguity. To reduce these three factors of uncertainties, we propose new paradigm with context-sensitive and hierarchical interpretation for homograph, multiple-interpretation for heuristic knowledge, and finally a certainty factor for data ambiguity. The validity of this paradigm is investigated by establishing a structure analysis system for drawing images with five hierarchical levels The interpretation proceeds from the lower level to the higher level in bottom-up manner using heuristic knowledge described as rules in a production system The heuristic knowledge is effectively used to compute or modify the certainty factor of multiple-interpretation in context-sensitive manner.