• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.81-92
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• 1997

The purpose of this paper is to develop a method of paramatric analysis that can be applied to an optimal solution of a maximum flow problem. We first define the transformed network corresponding to a given network. In such a network, we conduct paramatric analysis by determining changes in the optimal solution precipitated by changes in the capacity as the arc capacity varies from 0 to infinite. By this method we can easily calculate not only the characteristic region where the given optimal solution remains unchanged, but also the characteristic region where the value of the maximal flow gradually increases or decreases. The proposed method is demonstrated by numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.118-121
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• 2003

In the conventional boundary estimation in EIT (Electrical Impedance Tomography), the interface between anomalies and background is expressed in usual as Fourier series and the boundary is reconstructed by obtaining the Fourier coefficients. This paper proposes a method for the boundary estimation, where the boundary of anomaly is approximated as the interpolation of front points located discretely along the boundary and is imaged by tracking the points in the region of interest. In the solution to the inverse problem to estimate the front points, the multi-layer neural network is introduced. For the verification of the proposed method, numerical experiments are conducted and the results indicate a good performance.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.825-833
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• 1997

The purpose of this paper is to develop a method of postoptimality analysis that can be applied to an optimal solution of a maximum flow problem. We first use the transformed network corresponding to a given network. In such a network we conduct postoptimality analysis by determining changes in the optimal solution precipitated by changes in the capacity as the arc capacity varies from 0 to infinite. By this method we can easily calculate not only the characteristic region where the given optimal solution remains unchanged, but also the characteristic region where the value of the maximal flow gradually increases or decreases. The proposed method is demonstrated by numerical example.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.802-805
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• 2002

In the three-dimensional domain image expressed with two-dimensional slice images, such as fMRI images and multi-slice CT images, we propose the three-dimensional domain automatic segmentation for the purpose of extracting region. In this paper, we segmented each domain from the fMRI images of the head of people and monkey. We used the neural network "Pulse-Coupled Neural Network" which is one of the models of visual cortex of the brain based on the knowledge from neurophysiology as the technique. By using this technique, we can segment the region without any learning. Then, we reported the result of division of each domain and extraction to the fMRI slice images of human's head using "three-dimensional Pulse-Coupled Neural Network" which is arranged and created the neuron in the shape of a three-dimensional lattice.

• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.147-162
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• 2010

East Asian region, specifically China, Japan, and Korea, is considered as an area of advanced mobile handsets and mobile services. The well-established infrastructure of this region is well known due to rapid introduction of diverse feature-equipped handsets and advanced capabilities of mobile network operators. However, the status of mobile business has rarely been dealt with in previous studies. In this paper, we compare mobile value chains among these three countries. China has adopted open platform for mobile data services while Korea and Japan's mobile network operators control mobile portals for accessing diverse contents and services. We also discuss some possible reasons for the differences among the three countries in terms of value chain structures.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1581-1590
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• 1995

Recently, advanced video coding techniques using segmentation technique have been actively researched as candidates for video coding of MPEG-4 standard. The conventional segmentation techniques are unsuitable for real-time process because they have sequential structure. In this paper, we propose a new image segmentation technique using competitive learning neural network for vector quantization. The proposed segmentation procedure consist of prefiltering, primary and secondary segmentation, and a small region ellimination process. Primary segmentation segments input image in detail. Secondary segmentation merges similar region using a repetitive FSCL(Frequency sensitive competive learning) neural network. In this process, it is possible to segment an image from high resolution to low resolution by adjusting the number of repetition. Finally, small regions are merged into adjacent regions. Experimental results show that the procedure described yields reconstructed images of reasonably acceptable quality at bit rates of 0. 25 - 0.3 bit/pel.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.417-417
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• 2000

This paper proposes a face detection and recognition method that combines the template matching method and the eigenface method with the neural network. In the face extraction step, the skin color information is used. Therefore, the search region is reduced. The global property of the face is achieved by the eigenface method. Face recognition is performed by a neural network that can learn the face property.

• Journal of the Economic Geographical Society of Korea
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• v.23 no.1
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• pp.93-109
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• 2020

The purpose of this study is to examine the effects of industrialization and urbanization of Gwangyang Bay Region on the change of urban system and spatial structure between triangle-cities located in Gwangyang Bay, Yeosu City, Suncheon City, and Gwangyang City, one of the famous industrial zones in Korea. Large-scale development projects carried out by the central government in the Gwangyang Bay Region such as construction of the Second Oil Refinery in the mid-1960s, completion of the POSCO Gwangyang Steelworks in the mid-1980s, construction of the Gwangyang Port Container Terminal in 1987 and designation of the Gwangyang Bay Area Free Economic Zone in 2003, and EXPO 2012 Yeosu Korea, affected to changes of the urban system and spatial structure between triangle-cities in Gwangyang Bay Region. The above four-development projects transformed the urban and spatial structures between the three cities in the Gwangyang Bay Region from a mononuclear urban system centered on Suncheon to a network city system. Historically, Suncheon has served as an exclusive center in the eastern region of Jeonnam, including the Gwangyang Bay Region. However, the hosting of the 2012 Yeosu Expo Korea is reorganizing the three cities into a network-type spatial structure with the strengthening of connectivity and integration in the region. And this trend is expected to intensify in the future.