著者

森下 あおい 中村 顕輔

出版者

一般社団法人 日本デザイン学会

雑誌

デザイン学研究 (ISSN:09108173)

巻号頁・発行日

vol.65, no.3, pp.3_43-3_48, 2019-01-31 (Released:2019-03-25)

参考文献数

10

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服飾デザインの現場やデザイナー教育では,体形を客観的に把握しつつ創造性を高めるため,平均体形にデフォルマシオン(意匠的変形)を施した基準体形像が用いられる.しかし従来の研究は若年層のものに限られ,シニア層については未着手である.本報ではシニア女性の体形の多様性および体形を美しく見せる理想を反映した基準体形像を提案する.このため,3次元計測装置により集団計測したシニア女性53名の体形写真をデザイナーに観察させて体形分類を行い,シニア体形の特徴を顕著に有する3つの体形分類とそれらの代表体形を抽出した.3つの代表体形を別のデザイナーに見せて描かせたデザイン画に対して2次元骨格モデルを適用し,デフォルマシオンを定量的に分析することで基準体形像を抽出した.また基準体形像の妥当性を専門家の評価により確認した.

著者

崔 童殷 中村 顕輔 黒川 隆夫

雑誌

Journal of textile engineering (ISSN:13468235)

巻号頁・発行日

vol.52, no.6, pp.243-251, 2007-02-15

著者

森下 あおい 中村 顕輔

出版者

一般社団法人 日本デザイン学会

雑誌

デザイン学研究 (ISSN:09108173)

巻号頁・発行日

vol.61, no.6, pp.6_53-6_58, 2015-03-31 (Released:2015-07-31)

参考文献数

12

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服飾デザイン画に施されるデフォルマシオンを解明するため,実際の作品に描かれた人物像について頭身示数とプロポーションの関係を分析した.装苑賞の候補作品のうち,自然な立位姿勢の人物像が描かれたデザイン画131点を試料とした.デザイナーに試料の人体を推定させて,2次元骨格モデルを適応した.骨格モデルからプロポーションに関する人体寸法13項目を抽出した.頭身数および寸法を分析したところ,(1) 頭身数と各寸法の相関係数が小さいこと(r = 0.01~ 0.41),(2)高さ項目の寸法のばらつき割合(標準偏差/平均)は8.00%~16.68%であり,幅項目に比べて半分程度であること,また(3) 幅項目はばらつきが大きいが,互いに相関が大きいため(r = 0.72~0.87),ひとつのパラメータでひとつのパラメータで概ね説明できることなどが分かった.これらの結果より,試料のプロポーションのデフォルマシオンは頭の大きさおよび身体の幅で基本的に説明できると考えられる.

著者

崔 童殷 中村 顕輔 黒川 隆夫

出版者

一般社団法人 日本繊維機械学会

雑誌

繊維機械学会誌 (ISSN:03710580)

巻号頁・発行日

vol.58, no.6, pp.T68-T75, 2005

被引用文献数

9

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This paper proposes a method of simulating breast shape modification by wearing a brassiere. For this purpose a three-dimensional (3D) human body shape model developed by one of the authors is used. The model is made of a bi-cubic B-spline surface and its control points can describe a woman's trunk shape by fitting it to plenty of her body surface points. Corresponding control points among models have the same meaning in the sense that they form the same region on the different women's body surfaces. This feature enables us to compare and analyze a local body shape using a subset of control points selected appropriately. Forty-nine control points were determined to analyze breast shape of any woman. One hundred forty-two Japanese women aged 20's to 50's (brassiere size ranged from A70 to E70) were measured by means of an optical range finder before and after putting on a brassiere. We also categorized softness of their breasts into three classes; "soft", "medial", and "stiff". The multiple regression analysis established the relationship between the position of the control points on the models before and while wearing a brassiere using softness as a parameter. The best regression function was chosen among prepared 15 ones for each coordinate of every control points and was applied to simulation of brassiere-wearing breast figures based on the position of the control points before putting on it. The results showed that it is possible to estimate the body shape when wearing a brassiere. The same was confirmed based on average errors between silhouettes of brassiere-wearing and simulated breasts.

著者

崔 童殷 中村 顕輔 黒川 隆夫

出版者

一般社団法人 日本繊維機械学会

雑誌

Journal of Textile Engineering (ISSN:13468235)

巻号頁・発行日

vol.52, no.6, pp.243-251, 2006

被引用文献数

2

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The purpose of this research is to analyze Japanese women's breast shape based on body surface data described by a three-dimensional (3-D) human body shape model with a bi-cubic B-spline structure and to classify them. The data used for analysis were forty-nine 3-D control points selected from the right breast area on the model surface for each of 556 Japanese women aged 19 through 63 years. We examined the covariance matrix of the data using the principal component analysis method after normalization of their 3-D coordinates with the bust width for reducing the size factor. As a result, we obtained four principal components, which described 77% of breast shape. Then Japanese women's breast shape was classified into five classes in the principal component space using the first, second, third and forth-principal component scores. They could cover 92% of Japanese women's breasts. Therefore, we tried to analyze breast shape by clustering in order to classify all the breasts. For the cluster analysis we prepared two kinds of data; (1) principal component scores and (2) the normalized scores (μ=0, σ=1) of (1). With the clustering (1) and (2) we obtained four classes and five classes, respectively. Properties and advantages of the three kinds of classifications were also discussed. The classification of the principal component space is based on standard deviations of principal component scores, and therefore the resultant classes do not have clear boundaries. The classification according to the cluster analysis (1) can reflect the actual distribution of breast shape. In contrast the clustering (2) gives classification reflecting more principal components and tending to generate more classes than the clustering (1).

著者

崔 童殷 中村 顕輔 黒川 隆夫

出版者

一般社団法人 日本繊維機械学会

雑誌

Journal of Textile Engineering (ISSN:13468235)

巻号頁・発行日

vol.52, no.6, pp.243-251, 2006 (Released:2007-03-06)

参考文献数

12

被引用文献数

2

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The purpose of this research is to analyze Japanese women's breast shape based on body surface data described by a three-dimensional (3-D) human body shape model with a bi-cubic B-spline structure and to classify them. The data used for analysis were forty-nine 3-D control points selected from the right breast area on the model surface for each of 556 Japanese women aged 19 through 63 years. We examined the covariance matrix of the data using the principal component analysis method after normalization of their 3-D coordinates with the bust width for reducing the size factor. As a result, we obtained four principal components, which described 77% of breast shape. Then Japanese women's breast shape was classified into five classes in the principal component space using the first, second, third and forth-principal component scores. They could cover 92% of Japanese women's breasts. Therefore, we tried to analyze breast shape by clustering in order to classify all the breasts. For the cluster analysis we prepared two kinds of data; (1) principal component scores and (2) the normalized scores (μ=0, σ=1) of (1). With the clustering (1) and (2) we obtained four classes and five classes, respectively. Properties and advantages of the three kinds of classifications were also discussed. The classification of the principal component space is based on standard deviations of principal component scores, and therefore the resultant classes do not have clear boundaries. The classification according to the cluster analysis (1) can reflect the actual distribution of breast shape. In contrast the clustering (2) gives classification reflecting more principal components and tending to generate more classes than the clustering (1).