• International Journal of Human Ecology
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• v.15 no.2
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• pp.13-21
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• 2014

This study investigated the anthropometric characteristics of US women 26 to 45 years of age to classify their body shapes into different categories. Research data was obtained from 2950 women 26 to 45 years of age who participated in the SizeUSA study. A 26 to 35 years of age group and a 36 to 45 years of age group were selected from the data pool. A total of 26 measurements important for body shape classification and for apparel product development was used for the data analysis. Five factors accounted for the US women's body measurements. The body shapes of women were categorized into 4 types: Obese A-Shape, Overweight Y-Shape, Obese H-Shape, and Normal S-Shape. Normal S-Shape was the most common body shape type. More women in the 26 to 35 years of age group had Normal S-Shape type than women in the 36 to 45 years of age group. More women in the 36 to 45 years of age group had Obese A-Shape, Overweight Y-Shape, and Obese H-Shape than women in the 26 to 35 years of age group. Younger US women, 26 to 35 years of age had slimmer body sizes with more balanced body shapes; however, older US women, 36 to 45 years of age had larger body sizes with more various body shapes.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.725-744
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• 2015

In this paper, a new damage indicator based on mode shape data is introduced to identify damage in beam structures. In order to construct the indicator proposed, the mode shape, mode shape slope and mode shape curvature of a beam before and after damage are utilized. Mode shape data of the beam are first obtained here using a finite element modeling and then the slope and curvature of mode shape are evaluated via the central finite difference method. In order to assess the robustness of the proposed indicator, two test examples including a simply supported beam and a two-span beam are considered. Numerical results demonstrate that using the proposed indicator, the location of single and multiple damage cases having different characteristics can be accurately determined. Moreover, the indicator shows a better performance when compared with a well-known indicator found in the literature.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.23-32
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• 2007

We consider penalized likelihood regression with data from the negative binomial distribution with unknown shape parameter. Smoothing parameter selection and asymptotically efficient low dimensional approximations are employed for negative binomial data along with shape parameter estimation through several different algorithms.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.76-83
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• 2000

Shape reconstruction is considered as a new technology to be useful and important in many areas such as RPD (Rapid Product Development) and reverse engineering, compared with the conventional design and manufacturing. In shape reconstruction, it becomes possible to reconstruct objects not by their measured shape data but those data extracted from the original shape. The goal of this research is to realize 3D shape construction by showing a possible way to analyze the input image data and reconstruct that original shape. The main 2 steps of the reconstructing process are getting cross-section data from image processing and linking loops between one slice and the next one. And the reconstructed object in this way is compared with the other object using a laser scanner and modelled by an commercial software.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.4
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• pp.572-583
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• 2014

This study analyzes the foot shape of female high school students using 3D foot scan data based on a comparison with adult women (20s'-30s'). Data were collected from the foot anthropometry of 199 female high school students in Gwangju and Jeollanam-do. The right foot was measured indirectly by 3D laser scanner. There are 16 items in the foot anthropometric measurements. The $6^{th}$ Size Korea (measured by 3D scan data) is used for women's foot data. The results of the 3D measurements data investigation show that the foot length and foot width became longer and wider as the age increased. It is classified by three types after analyzing foot shape. Type 1 (28.1%) represented the shortest foot length, the narrowest foot width as well as the thick foot and long ankle shape. Type 2 (4.3%) represented the wide foot width such as the wide lateral ball width and semi-thickness shape. Type 3 (67.7%) referred to the widest foot width, flat foot and short ankle shape.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.758-765
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• 1999

Shape-preserving property is the important method that controls the complex free form curve/surface. Interpolation method for the existed Shape-Preservation had problems that it has needed the minimization of a curvature-related functions for calculating single-valued data. Solving this problem, in this paper, it proposed to the algorithm of generalizing C piecewise parametric cubic that has shape-preserving property for both Single-value data and Multivalue data. When there are the arbitrary tangents and two data, including shape-preserving property, this proposed method gets piecewise parametric cubic polynomial by checking the relation between the shape-preserving property and then calculates efficiently the control points using that. Also, it controls the initial shape using curvature distribution on curve segments.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.73-78
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• 2021

As interest in beauty has increased, various studies have been conducted, and related companies have considered the anthropometric data handled between humans and interfaces as an important factor. However, owing to the nature of 3D human body scanners used to extract anthropometric data, it is difficult to accurately analyze a user's body shape until a service is provided because the user only scans and extracts data. To solve this problem, the body shape of several users was analyzed, and the collected anthropometric data were obtained using a 3D human body scanner. After processing the extracted data and the anthropometric data, a custom deep learning model was designed, the designed model was learned, and the user's body shape information was predicted to provide a service suitable for the body shape. Through this approach, it is expected that the user's body shape information can be predicted using a 3D human body scanner, based upon which a beauty service can be provide.

• Journal of the Korea Fashion and Costume Design Association
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• v.21 no.4
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• pp.165-179
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• 2019

This study used 3D body scan data to classify body shapes according to the torso shape of adult males aged 20-75 years. This data will be provided so that the apparel industry can make apparel products corresponding to body characteristics by age. The study used 1,796 adult males between the ages of 20 and 75 and the 3D body shape data of the '5th Research on National Standard Anthropometry'. For data analysis, the program SPSSWIN Ver. 17.0 was used to calculate the mean and frequency allowing for a factor analysis, cluster analysis, analysis of variance, and Duncan test. To classify body shape according to the torso shape of adult males, this study considered nine factors: 'horizontal size of torso,' 'vertical size of body,' 'curve of torso and waist-abdomen flatness ratio,' 'length of torso,' 'shape of neck area,' 'degree of lateral curve,' 'difference between front and back interscye length,' 'shoulder armscye shape,' and 'chest flatness ratio.' Based on the results of the factor analysis, the torso shapes of adult males were classified into five types. Type 1 is "upright body with flat, curvy shape", Type 2 is "curve sway back body type", Type 3 is "flat, abdominally obese body", Type 4 is "obese, crooked body" and Type 5 is "thick sway front body type." named.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.416-421
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• 1997

This study presents a way to construct 3D shape in STL format from 2D slice data. Nowadays ahape reconstruct has been done in many ares, the application of this method is important especially in Reverse Engineering which reconstructs original shape from cross-section data. Current RP (Rapid Prototyping) is used not only for the verification of a part designed but also for the production and tooling in more effective way. In RP technology, data should be prepared in STL format. In this paper, the way to make 3D shape data in STL format form 2D slice data is described which can be used to reconstruct an original shape in RP equipment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.643-651
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• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).