• The korean journal of orthodontics
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• v.27 no.1
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• pp.119-127
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• 1997

The configuration of mandibular symphysis is likely to be dependent upon the genetic predeterminants and/or compensatory adjustments onto functional demands. The interrelation of morphological distinctives of symphysis in conjunction to the craniofacial skeleton had been scarcely anecdotal, therefore, the objective was to determine the correlation of morphological modifications between the mandibular symphysis and craniofacial complex. Lateral cephalometric headfilms of 212 subjects were employed for the conventional measurements. The proportion of chin height against chin depth length was referred as chin ratio, then, Low Symphysis (IS) and High Symphysis (HS) groups were turned out by means of the chin ratio. These samples yielded 35 in LS and 35 in HS groups. The data per capita were statistically analyzed and the following results were drawn ; 1. Overall characteristics of the craniofacial skeleton in HS group manifested hyperdivergence and LS group showed hypodivergence. 2. Gonial angle increased as chin ratio increased and was highly correlated to the chin ratio. 3. The chin ratio presented high correlation to the vertical face height, especially in terms of the chin height to anterior face height and the chin depth to posterior face height. 4. The morphological configuration of chin was hardly correlated with hyoid bone position.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.273-276
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• 2000

3차원 영상은 2차원 영상에 비해 데이터량이 매우 방대하다. 따라서 3차원 데이터를 효율적으로 압축하는 것은 매우 중요하다. 현재까지 대부분의 연구는 데이터량이 체적소(voxel)에 비해 월등히 적은 메쉬(mesh)를 기반으로 하여 이루어져 왔다. 하지만, 메쉬를 이용한 데이터 압축의 경우 체적소에 비해 데이터 자체의 규칙성이 떨어져 체적소를 이용한 압축에 비해 압축 효율이 낮다. 그리고, 체적소 데이터를 이용할 경우, 이를 스켈레톤화 하여 데이터량을 더욱 줄일 수 있다. 따라서 본 논문에서는 3차원 체 적소 데이터의 규칙성과 스켈레톤을 이용한 압축 기법을 제안할 것이다.

• Annual Conference of KIPS
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• 2007.11a
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• pp.101-103
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• 2007

혈관 구조는 사람의 가장 복잡한 구조 중 하나이다. 혈관 분석에는 Morphology and Topology 가 있다. 우리는 목표는 위 분석 기법과는 달리 3D 영상 재구성이다. 본 논문은 Raw CT data 을 세그먼트하고 Skeleton line 을 인용하여 복잡한 트리 형태의 혈관 3D 재구성을 하였다.

• Journal of the Korea Computer Graphics Society
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• v.21 no.3
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• pp.1-10
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• 2015

One way to create a visually immersive environment is to utilize a front projection system. Especially, when enough space is not available behind the screen, it becomes difficult to install a back projection system, making the front projection an appropriate choice. A drawback associated with the front projection is, however, the interference of shadow. The shadow can be cast on the screen when the user is located between the screen and the projector. This shadow can negatively affect the user experience and reduce the sense of immersion by removing important information. There have been various attempts to eliminating shadows cast on the screen by using multiple projectors that compensate for each other with missing information. There is trade-off between calculataion time and desired accuracy in this mutual compensation. Accurate estimation of the shadow usually requires heavy computation while simple approaches suffer from inclusion of non-shadow regions in the result. We propose a novel approach to removing shadows created in the front projection system using the skeleton data obtained from a depth camera. The skeleton data helps accurately extract the shape of the shadow that the user cast without requiring much computation. Our method also utilizes a distance field to remove the afterimage of shadow that may occur when the user moves. We verify the effectiveness of our system by performing various experiments in an interactive environment created by a front projection system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.127-133
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• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.1-9
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• 2001

We present a scheme and a data structure that decompose the space into adaptive-sized cells to improve the visualization of soft objects. Soft objects are visualized through the evaluation of the field functions at every point of the space. According to the propsed scheme, the affecting soft objects for a point in the space is searched through the data structure called interval tree based on the bounding volume of the components, which represent a soft object whose defining primitive(skeleton) is a simple geometric object such as point or line segment. The bounding volume of each component is generated with respect to the radius of a local field function of the component, threshold value, and the relations between the components and other neighboring components. The proposed scheme can be used in many applications for soft objects such as modeling and rendering, especially in interactive modeling process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.165-181
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• 2009

This paper presents an effective representation scheme for the shape analysis of the hippocampal structure and a stereoscopic-haptic environment to enhance sense of realism. The parametric model and the 3D skeleton represent various types of hippocampal shapes and they are stored in the Octree data structure. So they can be used for the interactive shape analysis. And the 3D skeleton-based pose normalization allows us to align a position and an orientation of the 3D hippocampal models constructed from multimodal medical imaging data. We also have trained Support Vector Machine (SVM) for classifying between the normal controls and epileptic patients. Results suggest that the presented representation scheme provides various level of shape representation and the SVM can be a useful classifier in analyzing the shape differences between two groups. A stereoscopic-haptic virtual environment combining an auto-stereoscopic display with a force-feedback (or haptic) device takes an advantage of 3D applications for medicine because it improves space and depth perception.

• The Research Journal of the Costume Culture
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• v.6 no.3
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• pp.201-216
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• 1998

This study is aimed at exploring a reasonable and reliable method of measuring pre-school children's somatotypes and there by, data basing the information obtained and classifying their somatotypes, at providing useful data which can be utilized for the design of their dress forms and enhancing the fitness of their apparels. to this end, 330 pre-school children living in the capital area and aged fro m4 to 6 were sampled to be subject to the measurement of their somatotypes. The results of this study can be summarized as follows; 1. As the pre-school children grow, the scales indicating their vertical growth including height could well be measured differently, but those scales indicating their lateral somatotypes which reflect their postural changes did not show among age groups. in other words, male kids were higher in the scales including height than female kids, while there were not differences between sexes in most scales indicating their lateral somatotypes. 2. The elements comprising the somatotypes were the size of body skeleton, the thickness of body mass, the posture and shape of body mass, the lateral under-neck shape, the extrusion of belly, the length between front and the back shoulder, the shape of lower belly, the shape of upper hip, the shape of lower hip and the slope of shoulders. Among them, the first two elements accounted for 64.8% of the total distribution, which means that these two elements explain the body-mass somatotypes of kid's most effectively. 3. The sample kids were divided into two types for classification of their somatotypes. As a result, it was found that the elements determining their somatotypes most influentially are, unlike adults' case the size of body skeleton rather than posture or lateral body shape. The type I showed less dimensions in most scales than type II, while their shoulder were les developed,. The type I was found distributed much in 4-year-old female kids. The type II showing more development in each element was found distributed much in 6-year-old male kids.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.87-93
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• 2023

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.