• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.1-8
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• 2005

Facial analysis is used in many applications like face recognition systems, human-computer interface through head movements or facial expressions, model based coding, or virtual reality. In all these applications a very precise extraction of facial feature points are necessary. In this paper we presents a method for automatic extraction of the facial features Points such as mouth corners, eye corners, eyebrow corners. First, face region is detected by AdaBoost-based object detection algorithm. Then a combination of three kinds of feature energy for facial features are computed; valley energy, intensity energy and edge energy. After feature area are detected by searching horizontal rectangles which has high feature energy. Finally, a corner detection algorithm is applied on the end region of each feature area. Because we integrate three feature energy and the suggested estimation method for valley energy and intensity energy are adaptive to the illumination change, the proposed feature extraction method is robust under various conditions.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.141-148
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• 2020

Automatic term extraction is to recognize domain-specific terms given a collection of domain-specific text. Previous term extraction methods operate effectively in unsupervised manners which include extracting candidate terms, and assigning importance scores to candidate terms. Regarding the calculation of term importance scores, the study focuses on utilizing sets of inner and outer terms of a candidate term. For a candidate term, its inner terms are shorter terms which belong to the candidate term as components, and its outer terms are longer terms which include the candidate term as their component. This work presents various functions that compute, for a candidate term, term strength from either set of its inner or outer terms. In addition, a scoring method of a term importance is devised based on C-value score and the term strength values obtained from the sets of inner and outer terms. Experimental evaluations using GENIA and ACL RD-TEC 2.0 datasets compare and analyze the effectiveness of the proposed term extraction methods for English. The proposed method performed better than the baseline method by up to 1% and 3% respectively for GENIA and ACL datasets.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.491-500
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• 2002

In this paper, we propose a real-time face detection and recognition system by using skin color informations, geometrical feature vectors of face, and facial angle informations from color face image. The proposed algorithm improved face region extraction efficiency by using skin color informations on the HSI color coordinate and face edge information. And also, it improved face recognition efficiency by using geometrical feature vectors of face and facial angles from the extracted face region image. In the experiment, the proposed algorithm shows more improved recognition efficiency as well as face region extraction efficiency than conventional methods.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.681-686
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• 2003

In recent years automatic image indexing and retrieval have been increasingly studied. However, content-based retrieval techniques for general images are still inadequate for many purposes. The novelty and originality of this thesis are the definition and use of a spatial information model as a contribution to the accuracy and efficiency of image search. In addition, the model is applied to represent color and shape image contents as a vector using the method of image features extraction, which was inspired by the previous work on the study of human visual perception. The indexing scheme using the color, shape and spatial model shows the potential of being applied with the well-developed algorithms of features extraction and image search, like ranking operations. To conclude, user can retrieved more similar images with high precision and fast speed using the proposed system.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.871-879
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• 1997

The comversion of printed maps into computerixed data bases is an enormous rask. Thus the autmaotion of the conversion process is essential. Efficient computer representation of printed maps and line drawings depends on codes assigened to chracaters, symbools, and vestor representation of the graphics. In many cases, maps ard constructed in a number of layers, where each layer is printed in a distinct color, and it represents a subste of the map infromation. In order to properly repressnet road information from color map images, an automatic road extraction algorithm is proposed. Road image is separated from graghics by color segmentation, and then restored by the proposed concurrent conditional dilation operation. The internal and external noise of the road image is eliminated by opening and closing operation. By thining and vectorizing line segments, the desited road information is extracted.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1252-1255
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• 2002

In this paper, face recognition algorithm, by using skin color information of HSI color coordinate collected from face images, elliptical mask, fratures of face including eyes, nose and mouth, and geometrical feature vectors of face and facial angles, is proposed. The proposed algorithm improved face region extraction efficacy by using HSI information relatively similar to human's visual system along with color tone information about skin colors of face, elliptical mask and intensity information. Moreover, it improved face recognition efficacy with using feature information of eyes, nose and mouth, and Θ1(ACRED), Θ2(AMRED) and Θ 3(ANRED), which are geometrical face angles of face. In the proposed algorithm, it enables exact face reading by using color tone information, elliptical mask, brightness information and structural characteristic angle together, not like using only brightness information in existing algorithm. Moreover, it uses structural related value of characteristics and certain vectors together for the recognition method.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.255-256
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• 2006

Raman spectroscopy has strong potential for providing noninvasive dermatological diagnosis of skin cancer. In this study, we investigated two classification methods with maximum a posteriori (MAP) probability and multi-layer perceptron (MLP) classification. The classification framework consists of preprocessing of Raman spectra, feature extraction, and classification. In the preprocessing step, a simple windowing method is proposed to obtain robust features. Classification results with MLP involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic Basal Cell Carcinoma (BCC) detection.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.30-39
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• 1998

Currently, LAMA(Local Automatic Message Accounting) is a billing mechanism which is being used for billing process, in which billing dat is written on MT(Magnetic Tape), collected at each regional billing center and then processed once a month. However, in order to prepare further development of telecommunication network and future new services, computerization of billing process and new daily billing processing system are urgently needed. Therefore Korea Telecom had developed a CAMA (Centralized Automatic Message Accounting) system for M10CN switch. In the CAMA system, billing data generated in switches is transfered on-line using approproate protocols and various transmission media and immediately processed. The CAMA system for M10CN switch consists of three parts such as ED (Extraction Device), TD(Transmission Device) and HC(Host Collector). The system also has redundancy to maintain continuance and confidence of system operation.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2914-2916
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• 1999

A power distribution facility map is composed of distribution facility information regions, cadastral regions and grid lines. In this map, our goal is automatic interpretation of power distribution region. For the goal, it is the first work to extract the power distribution facility region. In this paper, we propose a method to extract power distribution facility regions. The proposed method is consist of two phases, binarization phase and extending phase. The first phase generate a power distribution facility image using threshold value. The image contains many part of power distribution facility regions, but not all. The second phase extends thinned image which is generated by appling thinning operator to the power distribution facility image.