Generating Shampoo Bottle Contour Shapes Conforming to Product Concepts Based on Pairwise Preferences
Generating Shampoo Bottle Contour Shapes Conforming to Product Concepts Based on Pairwise Preferences
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基于成对偏好生成符合产品概念的洗发水瓶轮廓形状
DOI：
10.11184/his.24.1_53
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
横山 卓未
中科院分区：
文献类型：
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作者：
城下 慧人;小森 政嗣;横山 卓未
本研究的目的是估计心理效用函数，将产品设计转化为印象使用偏好学习方法，并构建产品形状匹配的产品概念的基础上估计的功能。使用椭圆傅立叶描述符（EFD），我们将26个洗发水瓶形状的轮廓转换为傅立叶系数，并对系数进行主成分分析（PCA）来构建形状空间。12人参加了五个实验会议，对应于五个不同的产品概念。在每个阶段，参与者都会看到一对随机生成的洗发水瓶图像。他们被要求在100次试验中选择与给定产品概念相匹配的产品。基于高斯过程偏好学习估计的平均效用函数，合成符合产品概念的瓶形。对合成的瓶形进行评估，以确定它们是否传达了预期的产品概念。结果表明，我们的方法是一种有效的方法来反映产品的概念，在形状设计。
This study aimed to estimate psychological utility functions that convert product design into impressions using preference learning methodology, and to construct product shapes that match product concepts based on the estimated functions. Using Elliptic Fourier descriptors (EFDs), we converted the contours of 26 shampoo bottle shapes into Fourier coefficients and performed a Principal Component Analysis (PCA) on the coefficients to construct shape space. Twelve persons participated in five experimental sessions corresponding to five different product concepts. For each session, participants were presented with a pair of randomly generated images of shampoo bottles from the shape space. They were asked to choose the one that matched a given product concept for 100 trials. The bottle shapes conforming to the product concepts were synthesized based on the average utility functions estimated by using Gaussian process preference learning. The synthesized bottle shapes were assessed to determine if they conveyed the intended product concepts. The results suggested that our approach is an effective way to reflect the product concept in the shape design.