• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.8
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• pp.1262-1272
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• 2009

The study is classifies the figures of obese women aged 20-50 with an over 25 BMI from the data of the fifth Size Korea in 2005. As the result of conducting the factor analysis for segmenting the shape, Factor 1, Factor 2, Factor 3, and Factor 4 are respectively derived as the factor on a volume, the factor on the size of the vertical direction, the factor on the shoulder region, and the factor on the body length balance. As the result of conducting the cluster analysis using 4 factors (scores extracted from the analysis of factor analysis) the body type of obese women was classified into four types. The name of shape was specified by combining 'P' (an abbreviation of petite) that indicated the height (smaller than 155cm) among the height names of KS standard, 'R' (abbreviation of regular) that indicated the height (155cm-165cm) and the body characteristics. Type 1 had the longest length, and normal circumference, thickness, and width but with the developed shoulder. Type 1 was classified as a robust, 'Plus-RH'. Type 2 had the middle height, the shortest length of the upper part, a relatively-long length of the lower part of body. Type 2 shows the characteristics of a small body that was classified as 'Plus-PI'. The most obese body was Type 3 that had the normal length and shoulder size but showed the longest length of the upper part of the body; it was classified as 'Plus-PO'. Type 4 as the small shape had a potbelly and showed the characteristics of the shortest body classified as 'Plus-Pb'.

• Fashion & Textile Research Journal
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• v.22 no.4
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• pp.495-503
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• 2020

This study classified lower body shape according to thigh and waist shape to improve the fit of skinny blue jeans in adult women in their 20s. We analyzed the three-dimensional automatic measurement data, three-dimensional indirect measurement data, and index data using the three-dimensional female (20-29 years old) body scan data provided by Size Korea (6^th Korean Human Dimensional Survey Project). Factor analysis was performed to classify body type. We selected and analyzed 34 items related to thigh shape based on index items, angle items, and protrusion amount items from 99 items; consequently, seven factors were extracted and 82.39% of the total variance was explained. Cluster analysis according to factor analysis classified it into 4 types, and a post-test Duncan test was conducted to classify thigh features according to classified types. As a result, the characteristics of lower body shape according to the thigh types of women in their 20s are as follows. Lower Body Type 1 is shape with a more prominent belly and less prominent thighs. Lower Body Type 2 is a slender body figure with larger hips. Lower Body Type 3 has more prominent thighs compared to the waist and belly. Lower Body Type 4 has both a prominent belly and prominent thighs.

• Journal of dental hygiene science
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• v.23 no.3
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• pp.193-198
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• 2023

Background: The nose is located at the center of the face, and it is possible to determine race, sex, and the like. Research using the nasal index (NI) classification method to classify the shape of the nose is currently in progress. However, domestic research is required as most research is being conducted abroad. In this study, we used a 3D program to confirm the ratio of the nose shape of Koreans. Methods: One hundred patients (50 males and 50 females) in their 20s were evaluated (IRB approval no. DKUDH IRB 2020-01-007). Cone beam computed tomography was performed using the Mimics ver.22 (Materialise Co., Leuven, Belgium) 3D program to model the patient's skull and soft tissues into three views: coronal, sagittal, and frontal. To confirm the ratio of measurement metrics, analysis was performed using the SPSS ver. 23.0 (IBM Co., Armonk, NY, USA) program. Results: Ten leptorrhine (long and narrow) type, 76 mesorrhine (moderate shape) type, and 14 platyrrhine (broad and short) type noses were observed. In addition, as a result of sex comparison, five males had the leptorrhine (long and narrow) type, 40 mesorrhine (moderate shape), and five platyrrhine (broad and short) types. For females, five patients had the leptorrhine (long and narrow) type, 36 patients had the mesorrhine (moderate shape) type, and nine patients had the platyrrhine (broad and short) type. Conclusion: This study will be helpful when performing nose-related surgeries and procedures in clinical practice and for similar studies in the future.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.411-417
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• 2018

Background Accessory auricles (AAs) are common congenital anomalies. We present a new classification according to location and shape, and propose a system for coding the classifications. Methods This study was conducted by reviewing the records of 502 patients who underwent surgery for AA. AAs were classified into three anatomical types: intraauricular, preauricular, and buccal. Intraauricular AAs were divided into three subtypes: intracrural, intratragal, and intralobal. Preauricular AAs were divided into five subtypes: precrural, superior pretragal, middle pretragal, inferior pretragal, and prelobal. Buccal AAs were divided into two subtypes: anterior buccal and posterior buccal. AAs were also classified according to their protrusion pattern above the surrounding surface: pedunculated, sessile, areolar, remnant, and depressed. Pedunculated and sessile AAs were subclassified as spherical, ovoid, lobed, and nodular, according to their body shape. Cartilage root presence and family history of AA were reviewed. A coding system for these classifications was also proposed. Results The total number of AAs in the 502 patients was 1,003. Among the locations, the superior pretragal subtype (27.6%) was the most common. Among the protrusion patterns and shapes, pedunculated ovoid AAs were the most common in the preauricular (27.8%) and buccal areas (28.0%), and sessile lobed AAs were the most common in the intraauricular area (48.7%). The proportion of AAs with a cartilage root was 78.4%, and 11% of patients had a family history. The most common type of preauricular AA was the superior pretragal pedunculated ovoid AA (13.2%) with a cartilage root. Conclusions This new system will serve as a guideline for classifying and coding AAs.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.300-311
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• 2014

According to fast change of the environment, the structured study of the ecosystem by analyzing the plant leaves are needed. Expecially, the methodology that searches and classifies the leaves from captured from the smart device have received numerous concerns in the field of computer science and ecology. In this paper, we propose a plant leaf classification technique using shape descriptor by combining Scale Invarinat Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) from the image segmented from the background via Graphcut algorithm. The shape descriptor is coded in the field of Locality-constrained Linear Coding to optimize the meaningful features from a high degree of freedom. It is connected to Support Vector Machines (SVM) for efficient classification. The experimental results show that our proposed approach is very efficient to classify the leaves which have similar color, and shape.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.417-422
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• 1998

This paper presents a method of object classification from dynamic image based on fuzzy inference algorithm which is suitable for low speed such as, conveyor, uninhabited transportation. At first, by using feature parameters of moving object, fuzzy if - then rule that can be able to adapt the wide variety of surroundings is developed. Secondly, implication function for fuzzy inference are compared with respect the proposed algorithm. Simulation results are presented to testify the performance and applicability of the proposed system.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.522-524
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• 2012

For exact classification of the defect, good feature selection and classifier is necessary. In this paper, various features such as brightness features, shape features and statistical features are stated and Bayes classifier using Gaussian mixture model is used as classifier. Also feature extraction method based on spectral graph theory is presented. Experimental result shows that feature extraction method using graph Laplacian result in better performance than the result using PCA.

• Journal of Society of Preventive Korean Medicine
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• v.4 no.2
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• pp.93-107
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• 2000

Korea follows the Korea standard classification of disease and causes of death according to the ICD(international classification of disease) Oriental medicine began to of officially follow the classification of disease for using the Korean classification of diseases in 1972. The classification of OM(oriental medicine) has changed in shape experiencing two amendments. The largest difficulty was to overcome the different names of diseases between OM and ICD. A one-to-one correspondence of the name of a disease between OM and ICD is impossible So in the primary stage one-to-one and one-to-many correspondence was made. During the first amendment the international disease names were re-classified on the oriental medicine disease name's basis and at the same time the classification of OM was corresponded on a one-to-one basis to the ICD . During the second amendment this changed to many-to-many correspondence . Analyzing the history of classification of OM during the first and second amendments, it was discovered that establishment of the standards of classification, the unification of oriental medical terms, and overcoming the difference of disease names between the OM and ICD is necessary Also th classification and standardazation of OM must not stop as a single round. It must go on for a long time. The hosts of this project Korean oriental medical society and AKOM(association of korean oriental medicine) need to build a independant department which will supervise the classification project and monitor any problems to come up. Also a route through which suggestions can be taken in and new solutions can be brought up needs to be secured and an atmosphere in which studies can take place about the basis of classifications needs to be developed.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.505-514
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• 2002

In this Paper, we propose a method of content-based image classification using neural network. The images for classification ate object images that can be divided into foreground and background. To deal with the object images efficiently, object region is extracted with a region segmentation technique in the preprocessing step. Features for the classification are texture and shape features extracted from wavelet transformed image. The neural network classifier is constructed with the extracted features and the back-propagation learning algorithm. Among the various texture features, the diagonal moment was more effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows correct classification rates of 72.3% and 67%, respectively.

• KCID journal
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• v.17 no.2
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.