MISC

査読有り
2019年

畳み込みニューラルネットワークを用いた自動車の三次元モデルにおける各車型の特徴抽出と視覚化

日本感性工学会論文誌
  • 田中 俊太朗
  • ,
  • 原田 利宣
  • ,
  • 小野 謙二

18
1
開始ページ
113
終了ページ
121
記述言語
日本語
掲載種別
DOI
10.5057/jjske.TJSKE-D-18-00039
出版者・発行元
日本感性工学会

The cars are classified by cars' body types. However the characteristics are basically similar at first sight, so it is difficult to distinguish the differences among those cars' body types. Therefore, in this study, we considered that cars' characteristics could be analyzed by using deep learning and image recognition technology, developed a system to visualize the judgment and characteristic parts of cars' body types. Specifically, we made renderings of the CG model of 30 cars by setting 360 viewpoints in 1 degree increments around each car. Deep learning was performed using these 2D images as teacher signals. The car body type recognition probability of each angle is graphed, and the characteristic parts of each car body type are visualized. As a result, we clarified the visual angles and the pars contributing the judgment of cars' body types.

リンク情報
DOI
https://doi.org/10.5057/jjske.TJSKE-D-18-00039
CiNii Articles
http://ci.nii.ac.jp/naid/130007605467
ID情報
  • DOI : 10.5057/jjske.TJSKE-D-18-00039
  • ISSN : 1884-0833
  • CiNii Articles ID : 130007605467
  • identifiers.cinii_nr_id : 9000399813688

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