• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.43-52
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• 1998

We extract a hand region by using color information, which is an important feature for human vision to distinguish objects. Because pixel values in images are changed according to the luminance and lighting source, it is difficult to extract a hand region exactly without previous knowledge. We generate a hand skin model at learning stage, and extract a hand region from images by using the model. We also use a Kalman filter to consider changes of pixel values in a hand skin model. A Kalman filter restricts a search area for extracting a hand region at next frame also. The validity of the proposed method is proved by implementing the hand-region acquisition module.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.77-85
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• 2009

In this paper, we propose data format for smell, taste, touch with speech and vision which can be transmitted and implement a floral scent detection and recognition system. We provide representation method of data of smell, taste, and touch. Also, proposed floral scent recognition system consists of three module such as floral scent acquisition module using Metal Oxide Semiconductor (MOS) sensor array, entropy-based floral scent detection module, and floral scent recognition module using correlation coefficients. The proposed system calculates correlation coefficients of the individual sensor between feature vector(16 sensors) from floral scent input point until the stable region and 12 types of reference models. Then, this system selects the floral scent with the maximum similarity to the calculated average of individual correlation coefficients. To evaluate the floral scent recognition system using correlation coefficients, we implemented an individual floral scent recognition system using K-NN with PCA and LDA that are generally used in conventional electronic noses. In the experimental results, the proposed system performs approximately 95.7% average recognition rate.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.211-218
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• 2001

This paper introduces a method of constructing expert systems in an integrated environment for automatic software design. This integrated environment may be applicable from top-level system architecture design, data flow diagram design down to flow chart and coding. The system is integrated with three CASE tools, FSD (Functional Structure Diagram), DFD (Data Flow Diagram) and structured chart PAD (Problem Analysis Diagram), and respective expert systems with automatic design capability by reusing past design. The construction way of these expert systems is based on systematic acquisition of design knowledge stemmed from a systematic design work process of well-matured developers. The design knowledge is automatically acquired from respective documents and stored in the respective knowledge bases. By reusing it, a similar software system may be designed automatically. In order to develop these expert systems in a short period, these design knowledge is expressed by the unified frame structure, functions of th expert system units are partitioned mono-functions and then standardized components. As a result, the design cost of an expert system can be reduced to standard work procedures. Another feature of this paper is to introduce the integrated environment for automatic software design. This system features an essentially zero start-up cost for automatic design resulting in substantial saving of design man-hours in the resulting in substantial saving of design man-hours in the design life cycle, and the expected increase in software productivity after enough design experiences are accumulated.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1792-1802
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• 1996

The analysis of prepositional phrases in English-to Korean machine translation has problem on the PP-attachment resolution, semantic analysis, and acquisition of information. This paper presents an analysis system for prepositional phrases, which solves the problem. The analysis system consists of the PP-attachment resolution hybrid system, semantic analysis system, and semantic feature generator that automatically generates input information. It provides objectiveness in analyzing prepositional phrases with the automatic generation of semantic features. The semantic analysis system enables to generate natural Korean expressions through selection semantic roles of prepositional phrases. The PP-attachment resolution hybrid system has the merit of the rule-based and neural network-based method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1080-1087
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• 2009

This paper presents an algorithm to acquire 3D geometric information using a virtual plane method. The method to measure 3D information on the plane is easy, because it's not concerning value on the z-axis. A plane can be made by arbitrary three points in the 3D space, so the algorithm is able to make a number of virtual planes from feature points on the target object. In this case, these geometric relations between the origin of each virtual plane and the origin of the target object coordinates should be expressed as known homogeneous matrices. To include this idea, the algorithm could induce simple matrix formula which is only concerning unknown geometric relation between the origin of target object and the origin of camera coordinates. Therefore, it's more fast and simple than other methods. For achieving the proposed method, a regular pin-hole camera model and a perspective projection matrix which is defined by a geometric relation between each coordinate system is used. In the final part of this paper, we demonstrate the techniques for a variety of applications, including measurements in industrial parts and known patches images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.449-457
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• 2011

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.41-49
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• 2007

BCI system integrates control or telecommunication system with generating electric signals in scalp itself after signal acquisition. This system detect a movement of EEG at real time, can control an electron equipment using a generated signal through EEG movement or software-based processor. In this paper, we deal with removing and separating artifacts induceced from measurement when brain-computer interface system that analyzes recognizes EEG signals occurred from various mental states. In this paper, we proposed a method of EEG classification and an artifact interval detection using bisection mathematical modeling in the EEG classification process for BCI system implementation.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.23-28
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• 2002

A fundamental problem in building an intelligent agent is that an agent does not understand the meaning of its perception or its action. One reason that an agent cannot understand the world is partially caused by a syntactic approach that converts a semantic feature into a simple string. To solve this problem, Cohen introduces a semantic approach that an agent autonomously learns a meaningful representation of physical schemas, on which some advanced conceptual structures are built, from physically interacting with environment using its own sensors and effectors. However, Cohen does not deal with a meta level of conceptual primitive that makes recognizing a schema possible. We propose that negation is a meta schema that enables an agent to recognize a physical schema. We prove some semantic aspects of negation.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.364-371
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• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.