• Journal of Advanced Technology Convergence
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• v.3 no.1
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• pp.65-72
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• 2024

Study of the effect of interbody stabilization exercises on the static and dynamic balance of face-to-face, non-face-to-face, and self-group. We recruited healthy young adults The subjects were randomly divided into three groups to perform inter-body stabilization exercises, and static and dynamic balance were measured, respectively, before and after intervention. In the Face to Face Group, dynamic balance significantly increased in the anterior and posteriomedial directions before and after intervention. There was also a significant increase in static balance. In the Non face to face Group, there was a significant increase in the three directions of dynamic and static balance. The self-exercise group significantly increased in three directions of dynamic balance, but there was no significant difference in static balance. No significant differences between groups were found in any variables. The results of this study showed that interbody stabilization exercises are effective in improving static and dynamic balance. In the face-to-face group, the inter-body stabilization exercise was effective in improving dynamic balance and static balance, and in the non-face-to-face group, the inter-body stabilization exercise was effective in improving dynamic balance and static balance. In the self-contained group, it was effective in improving dynamic balance.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.252-258
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• 2007

In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.835-838
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• 2003

Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.185-192
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• 1999

This paper presents the results of a parametric study on the behavior of tunnel face reinforced with horizontal pipes. A three-dimensional finite element model was adopted in this study to capture the three-dimensional nature of tunnel face behavior under various boundary conditions. A parametric study was peformed on a wide range of boundary conditions with emphasis on the effect of reinforcing layouts on the deformation behavior of tunnel face. The results of analysis such as tunnel face deformation behavior under various conditions were thoroughly analyzed, and a database for the behavior of tunnel face under different reinforcing conditions was established for future development of a semi-empirical design/analysis method for the tunnel face reinforcing technique. The results indicated that there exits an optimum reinforcing layout for a given tunnel condition, which must be selected with due consideration of tunnel geometry and ground condition.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.457-464
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• 2000

Stereographic method is a general and basic method to analyse sliding failure potential of rock slope. Region of failure analysis using stereographic method extend to curved slope from straight slope in this paper, Curved slope is defined as the multi-face slope with free surface more than two face and has different characteristics from straight single face slope. Individual daylight envelopes of free surfaces are combined into total daylight envelope of multi-face slope. So, sliding envelope of multi-face slope is the daylight envelope except friction cone. Specially, If only single joint set is developed in the slope, single plane sliding(or plane failure) is impossible in the single-face straight slope, but possible in the multi-face slope. In the multi-face slope with only one joint set, single plane sliding occurs when orientation of sliding plane is between two side slope orientation in the sliding envelope.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.333-334
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• 2008

A real-time face tracking is a broad topic, covering a large spectrum of technologies and applications. Briefly face tracking is a kind of tracing technique which follows human face in any directions. It needs some algorithms such as human face detection and motion controller to track face. Moreover, both processing time and calculation time are the most important factors that influence to drive tracking system. In this paper, two algorithms are used to find human face: earn-shift algorithm and face detection algorithm using OpenCV. Fuzzy controller is utilized to move pan-tilt camera system which can move four directions along to x-y axis.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1689-1694
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• 2005

Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.521-524
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• 2003

In this paper, we propose a face region extraction algorithm using color information and projection. After the extraction of face candidate image using adaptive color information, we project it into vertical direction to estimate the width of the face. Then the redundant parts of the face are efficiently removed by using the estimated face width. And the width information of the face is used at the horizontal projection step to extract the height of the face, and non-face region such as the neck and some background regions, which are represented as the similar skin color, effectively eliminated. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.124-127
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• 1997

In this paper, we propose the new face detection and tracking angorithm in sequential images which have complex background. In order to apply face deteciton algorithm efficently, we convert the conventional RGB coordiantes into CIE coordonates and make the input images insensitive to luminace. And human face shapes and colors are learned using ueural network's backpropagation. For variable face size, we make mosaic size of input images vary and get the face location with various size through neural network. Besides, in sequential images, we suggest face motion tracking algorithm through image substraction processing and thresholding. At this time, for accurate face tracking, we use the face location of previous. image. Finally, we verify the real-time applicability of the proposed algorithm by the simple simulation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.47-53
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• 2010

This paper proposed Residual Image detection and Color Info using the face detection technique. The proposed technique was fast processing speed and high rate of face detection on the video. In addition, this technique is to detection error rate reduced through the calibration tasks for tilted face image. The first process is to extract target image from the transmitted video images. Next, extracted image processed by window rotated algorithm for detection of tilted face image. Feature extraction for face detection was used for AdaBoost algorithm.