• DocumentCode
    3464405
  • Title

    Statistical Shape Analysis of Headlights

  • Author

    Ishihara, Sayaka ; Ishihara, Koichi ; Nagamachi, M.

  • Author_Institution
    Dept. of Kansei Design, Hiroshima Int. Univ., Higashi-Hiroshima, Japan
  • fYear
    2011
  • fDate
    19-22 Sept. 2011
  • Firstpage
    27
  • Lastpage
    32
  • Abstract
    In Kansei Engineering, sample shapes have been treated as categorical variables in nominal scale. In this paper, we describe the method for focusing on product shapes concerning customers´ Kansei, to avoid confusing effects on Kansei caused by other properties such as sizes, orientations, colors, textures, and so on. We standardized the shapes of sample products and made them into statistical values with morphometric methods. This preprocessing of the shape let us build mathematical models of shape space and relations between shapes and Kansei based on the results of customers Kansei evaluations. Then, statistical analyses that are frequently used in Kansei Engineering, e.g., cluster analysis and principal component analysis, followed to reveal the characteristics of shape samples associated with specific Kansei and semantic space. Here, we described the proposed analysis method on headlights of passenger cars and present the resulting associations between concrete shapes and Kansei evaluation.
  • Keywords
    automotive components; design engineering; shapes (structures); statistical analysis; Kansei engineering; customers Kansei evaluations; headlights; mathematical models; morphometric methods; passenger cars; product shapes; semantic space; shape space; statistical shape analysis; Kernel; Mathematical model; Optimization; Principal component analysis; Shape; Shape measurement; Car Design; Kansei Engineering; Morphometrics; Shape Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biometrics and Kansei Engineering (ICBAKE), 2011 International Conference on
  • Conference_Location
    Takamatsu, Kagawa
  • Print_ISBN
    978-1-4577-1356-9
  • Electronic_ISBN
    978-0-7695-4512-7
  • Type

    conf

  • DOI
    10.1109/ICBAKE.2011.52
  • Filename
    6031244