• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3136-3150
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• 2015

A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.521-523
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• 2012

In the field of virtual reality and game production with realistic, real-time character's behavior is frequently used. In this paper, the terrain changes or other actions on the surrounding environment by implementing adaptive any action that is the most natural and can be adapted to analyze real-time character animation is implemented. For this purpose, in order to control the various movements of the character of the human body, by highlighting the major joints Kinematic dummy character animation is to create a way. Changes in terrain height difference of the two stairs that could cause analysis of the behavior in the environment and the other on the surrounding environment by adapting behavior to analyze the behavior of the slope climbing Based on this, the character animation is implemented.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.136-143
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• 2013

In this paper, a algorithm to find joints of a small animal like a lizard from the multiple-view silhouette images is presented. The proposed algorithm is able to calculate the 3D coordinates so that the locomotion of the lizard is markerlessly reconstructed. The silhouette images of the lizard was obtained by a adaptive threshold algorithm. The skeleton image of the silhouette image was obtained by Zhang-Suen method. The back-bone line, head and tail point were detected with the A* search algorithm and the elimination of the ortho-diagonal connection algorithm. Shoulder joints and hip joints of a lizard were found by $3{\times}3$ masking of the thicked back-bone line. Foot points were obtained by morphology calculation. Finally elbow and knee joint were calculated by the ortho distance from the lines of foot points and shoulder/hip joint. The performance of the suggested algorithm was evaluated through the experiment of detecting joints of a small lizard.

• Journal of Periodontal and Implant Science
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• v.46 no.2
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• pp.96-106
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• 2016

Purpose: Stress distribution and mandible distortion during lateral movements are known to be closely linked to bruxism, dental implant placement, and temporomandibular joint disorder. The present study was performed to determine stress distribution and distortion patterns of the mandible during lateral movements in Class I, II, and III relationships. Methods: Five Korean volunteers (one normal, two Class II, and two Class III occlusion cases) were selected. Finite element (FE) modeling was performed using information from cone-beam computed tomographic (CBCT) scans of the subjects' skulls, scanned images of dental casts, and incisor movement captured by an optical motion-capture system. Results: In the Class I and II cases, maximum stress load occurred at the condyle of the balancing side, but, in the Class III cases, the maximum stress was loaded on the condyle of the working side. Maximum distortion was observed on the menton at the midline in every case, regardless of loading force. The distortion was greatest in Class III cases and smallest in Class II cases. Conclusions: The stress distribution along and accompanying distortion of a mandible seems to be affected by the anteroposterior position of the mandible. Additionally, 3-D modeling of the craniofacial skeleton using CBCT and an optical laser scanner and reproduction of mandibular movement by way of the optical motion-capture technique used in this study are reliable techniques for investigating the masticatory system.

• Journal of Platform Technology
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• v.12 no.2
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• pp.23-33
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• 2024

The development of mixed reality (MR) technology has a great influence on the research and development of medical support equipment. In particular, it supports to respond effectively to emergencies occurring in the field. MR technology enables access to first aid and field support by combining virtual information with the real world so that users can see virtual objects in the real world. However, due to the nature of the equipment, there is a limitation in accurately matching virtual objects based on user vision. To improve these limitations, this paper proposes a 3D biometric object recognition and matching algorithm in the MR environment. As a result of the experiment, when a virtual object is rendered and visualized while equipped with an optical-based HMD from the user's side, it was possible to reduce the user's field of view error and eliminate the joint-loss phenomenon during skeleton recognition. The proposed method can reduce errors between the real user's field of view and the virtual image and provide a basis for reducing errors that occur in the process of virtual object recognition and matching. It is expected that this study will contribute to improving the accuracy of the telemedicine support system for first aid.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.723-730
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• 2013

This paper presents the design of an arm gesture recognition system using Kinect sensor. A variety of methods have been proposed for gesture recognition, ranging from the use of Dynamic Time Warping(DTW) to Hidden Markov Models(HMM). Our system learns a unique HMM corresponding to each arm gesture from a set of sequential skeleton data. Whenever the same gesture is performed, the trajectory of each joint captured by Kinect sensor may much differ from the previous, depending on the length and/or the orientation of the subject's arm. In order to obtain the robust performance independent of these conditions, the proposed system executes the feature transformation, in which the feature vectors of joint positions are transformed into those of angles between joints. To improve the computational efficiency for learning and using HMMs, our system also performs the k-means clustering to get one-dimensional integer sequences as inputs for discrete HMMs from high-dimensional real-number observation vectors. The dimension reduction and discretization can help our system use HMMs efficiently to recognize gestures in real-time environments. Finally, we demonstrate the recognition performance of our system through some experiments using two different datasets.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.269-282
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• 2021

As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.