• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1639-1649
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• 2004

In this paper, a method for detection of forged signatures based on spectral analysis of directional gradient density function and a weighted fuzzy classifier is proposed. The well defined outline of an incoming signature image is extracted in a preprocessing stage which includes noise reduction, automatic thresholding, image restoration and erosion process. The directional gradient density function derived from extracted signature outline is highly related to the overall shape of signature image, and thus its frequency spectrum is used as a feature set. With this spectral feature set, having a property to be invariant in size, shift, and rotation, a weighted fuzzy classifier is evaluated for the verification of freehand and random forgeries. Experiments show that less than 5% averaged error rate can be achieved on a database of 500 signature samples.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.313-320
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• 1999

In this paper, a translation, rotation and scale invariant system for the classification of closed 2D images using the bispectrum of a contour sequence and the weighted fuzzy mean method is derived and compared with the classification process using one of the competitive neural algorithm, called a LVQ(Learning Vector Quantization). The bispectrun based on third order cumulants is applied to the contour sequences of the images to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images and are fed into an classifier using weighted fuzzy mean method. The experimental processes with eight different shapes of aircraft images are presented to illustrate the high performance of the proposed classifier.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.250-255
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• 2000

This paper is concerning off-line signature verification using a density function which is obtained by convolving the signature image with twelve-directional 5$\times$5 gradient masks and the weighted fuzzy mean classifier. The twelve-directional density function based on Nevatia-Babu template gradient is related to the overall shape of a signature image and thus, utilized as a feature set. The weighted fuzzy mean classifier with the reference feature vectors extracted from only genuine signature samples is evaluated for the verification of freehand forgeries. The experimental results show that the proposed system can classify a signature whether genuine or forged with more than 98% overall accuracy even without any knowledge of vaned freehand forgeries.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.592-603
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• 2000

This paper is concerning off-line signature verification using a density function which is obtained by convolving the signature image with twelve-directional $5\times{5}$ gradient masks and the weighted fuzzy mean classifier. The twelve-directional density function based on Nevatia-Babu template gradient is related to the overall shape of a signature image and thus, utilized as a feature set. The weighted fuzzy mean classifier with the reference feature vectors extracted from only genuine signature samples is evaluated for the verification of freehand forgeries. The experimental results show that the proposed system can classify a signature whether it is genuine or forged with more than 98% overall accuracy even without any knowledge of varied freehand forgeries.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.421-427
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• 2001

In this paper, a method for selection of the optimal feature vectors is proposed for the classification of closed 2D shapes using the bispectrum of a contour sequence. The bispectrum based on third order cumulants is applied to the contour sequences of the images to extract feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images, but there is no certain criterion on the selection of the feature vectors for optimal classification of closed 2D images. In this paper, a new method for selecting the optimal bispectral feature vectors based on the variances of the feature vectors. The experimental results are presented using eight different shapes of aircraft images, the feature vectors of the bispectrum from five to fifteen and an weighted mean fuzzy classifier.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.9-15
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• 2009

The fuzzy weighted mean classifier is one of the most common classification models and could achieve high performance by adjusting the weights. However, the weights were generally decided based on the experience of experts, which made the resulting classifiers to suffer the lack of consistency and objectivity. To resolve this problem, in this paper, a weight deciding method based on the statistics of the data is introduced, which ensures the learned classifiers to be consistent and objective. To investigate the effectiveness of the proposed methods, Iris data set available from UCI machine learning repository is used and promising results are obtained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.