Extraction of 3D Curved Tight and Flared Skirt Shape Features Using Angle Curvatures

Abstract

The 3D curved surface shapes of tight and flared skirts were predicted precisely by the angle curvatures (concentrated Gaussian curvature Kc, concentrated geodesic curvature kc, and concentrated mean curvature Hc), model sizes, skirt designs, and fabrics. All of the 72 skirts, encompassing 3 female body models (mean body sizes of Japanese women in their 20s, 40s, and 70s), 6 kinds of fabrics, tight skirts, and 3 kinds of flared skirts, were investigated with attention to the differences of the curved surface shapes in detail. It has been found that it is possible to predict the number of nodes on hemlines for these 72 skirts with the three totalized feature factors, |Σ±Kc|+|Σ±kc|, |Σ±Hc|, and weight (g/ cm 2) × hemline length (cm), underlying the physical properties of fabric and model sizes, based on slightly high or high correlation coefficients (r = 0.73 to r = 0.93). Therefore, the number of nodes on hemlines of these 72 skirts can be calculated by these three totalized feature factors using a multiple regression analyzing technique, (multiple regression coefficient R = 0.96), based on higher prediction accuracy.

Extraction of 3D Tight and Flared Skirt Curved Shapes by Relating Sensitivity Images and Physical Properties with 3D Curvature Values

“…1) The 72 3D skirts are constructed by 3 Japanese women in their 20s, 40s, and 70s body models [8][9][10], 6 kinds of fabrics (cotton broad (A), tropical wool (B), tropical polyester (C), cotton toile (D), polyester single-yarn twill (E) and polyester faille (F)), with a total 12 skirt patterns for the 4 skirt styles, (tight, flare 1, flare 2, and flare 3 skirts) in previous paper [11], as shown in appendix 2) The sensitivity images' DEn for the 72 skirt photographs (scale of 1 to 7), as shown in Table 2 and Fig. 1, are evaluated for the fabric curved shape images of DE1 to DE5 using the semantic differential (SD) method and the design curved shape images of DE6 to DE11 using the five-grade evaluation system method.…”

“…3) The physical properties of the 3D curvature parameters CKn and CHn, and the conventional fabric parameter for Mn are as follows. CKn and CHn show the design curved shape parameters of CK1 to CK4 (|Σ±Kc|+|Σ±kc|) and CH1 to CH4 (|Σ±Hc|) in each area of the back, front, sides of the right and left, and all total areas of the 72 skirts, and Mn represents the fabric curved shape parameters M1 to M6 [11] (see Table 2, appendix shape, Kc < 0 (-Kc); hyperbolic curved shape, Kc = 0; developable surface curved shape, kc > 0 (+kc); convex line curved shape, kc < 0 (-kc); concave line curved shape, kc = 0; straight line curved shape, Hc > 0 (convex surface shape +Hc); mountain fold curved shape, Hc < 0 (concave surface shape -Hc); valley fold curved shape, and Hc = 0; plane surface shape. The three fabric curved shape images of DE1 to DE3 had significant relationships with all six design curved shape images of We were able to determine that the fabric curved shape images of DE1 to DE3 influenced the design curved shape images of DE6 to DE11.…”

“…It is well known that the skirt fabric shape factors are extracted based on the fabric's dynamic characteristics and sensitivity image evaluations using the subjective assessments in many previous reports [1][2][3][4][5][6]. In a previous report [7], the investigation of the 3D (three-dimensional) tight-fitting skirt curved shape was developed based on a virtual reality skirt of a made-to-order system without the fabric's physical properties, and the four 3D tight-fitting skirt kinds of fabric for the 3 body model shapes (bodies of women in 20s, 40s, and 70s [8][9][10]), using an angle curvature method that references the 3D curvature parameters of |Σ±Kc|+|Σ±kc| (CKn) and |Σ±Hc| (CHn, where Hc is the concentrated mean curvature) and the conventional fabric parameters Mn in a previous report [11] (see appendix Fig.Ⅰ, and Fig.Ⅱ). We revealed the usefulness of the 3D…”

“…curvatures Kc, kc, and Hc using the common angles for extracting the features of many virtual reality 3D tight-fitting skirt shapes [7] and for predicting the nodes on the 72 3D tight and flared skirt hemlines in physical properties [11]. However, there is no report concerning both the sensitivity images and the physical properties of the many real skirt 3D curved shapes for the fabrics, female body model sizes, and the 3D curvatures of the common angles.…”

“…In this research, the features of the fabric and design curved skirt shapes in both the sensitivity images' DEn and the physical properties of CKn, CHn, and Mn [11] are examined for the purpose of extracting the useful data and information to develop a made-toorder system for a virtual-reality skirt that uses a real skirt shape and Masuda Tomoe, Wada Minami, Murakami Kaori, Yokura Hiroko 2 that supports communication between the retailers and consumers.…”