• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.751-754
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• 2000

In this paper, we propose an efficient method to detect abrupt shot changes in compressed MPEG video data by using reference ratios among video frames. The reference ratios among video frames imply the degree of similarities among adjacent frames by prediction coded type of each frames. A shot change is detected if the similarity degrees of a frame and its adjacent frames are low. This paper proposes an efficient shot change detection algorithm by using Fuzzy c-means(FCM) clustering algorithm. The FCM clustering uses the shot change probabilities evaluated in the mask matching of reference ratios and difference measure values based on frame reference ratios.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1536-1544
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• 1995

We propose a design scheme of a binary decision tree and apply it to the tire tread pattern recognition problem. In this scheme, a binary decision tree is constructed by using fuzzy C-means( FCM ) algorithm. All the available features are used while clustering. At each node, the best feature or feature subset among these available features is selected based on proposed similarity measure. The decision tree can be used for the classification of unknown patterns. The proposed design scheme is applied to the tire tread pattern recognition problem. The design procedure including feature extraction is described. Experimental results are given to show the usefulness of this scheme.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.173-179
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• 1998

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neural net, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Menas) arithmatics, to decide whether a requested call that is not trained in learning phase to be connected or not. The system generates the estimated traffic pattern of the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmatics. The input to the NN is the vector consisted of traffic parameters which is the means and variances of the number of cells arriving inthe interval. After training(using error back propagation algorithm), when the NN is used for decision making, the decision as to whether to accept or reject a new call depends on whether the output is greater or less then decision threshold(+0.5). This method is a new technique for call admi sion control using the membership values as traffic parameter which declared to CAC at the call set up stage, and is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simmulation. it is founded the performance of the suggested method outforms compared to the conventional NN method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.217-222
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• 2009

In this paper, a new approach is proposed for the segmentation of Computed Tomography (CT) head images. The approach consists of two-stage segmentation with each stage contains two different segmentation techniques. The ultimate aim is to segment the CT head images into three classes which are abnormalities, cerebrospinal fluid (CSF) and brain matter. For the first stage segmentation, k-means and fuzzy c-means (FCM) segmentation are implemented in order to acquire the abnormalities. Whereas for the second stage segmentation, modified FCM with population-diameter independent (PDI) and expectation-maximization (EM) segmentation are adopted to obtain the CSF and brain matter. The experimental results have demonstrated that the proposed system is feasible and achieve satisfactory results.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.94-99
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• 2008

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.35-41
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• 2007

In this paper, a modified Fuzzy C-Means (MFCM) algorithm is presented for nonlinear blind channel equalization. The proposed MFCM searches the optimal channel output states of a nonlinear channel, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. In its searching procedure, all of the possible desired channel states are constructed with the elements of estimated channel output states. The desired state with the maximum Bayesian fitness is selected and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA merged with simulated annealing (SA): GASA), and the relatively high accuracy and fast searching speed are achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.871-876
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• 2006

In this thesis, to accurately measure 3D face information using the spatial encoding patterns, the new algorithm to segment the pattern images from initial face pattern image is proposed. If the obtained images is non-homogeneous texture and ambiguous boundary pattern, the pattern segmentation is very difficult. Furthermore. the non-encoded areas by accumulated error are occurred. In this thesis, the FCM(fuzzy c-means) clustering method is proposed to enhance the robust encoding and segmentation rate under non-homogeneous texture and ambiguous boundary pattern. The initial parameters for experiment such as clustering class number, maximum repetition number, and error tolerance are set with 2, 100, 0.0001 respectively. The proposed pattern segmentation method increased 8-20% segmentation rate with conventional binary segmentation methods.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.109.6-109
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• 2002

In this paper, the scheme of fuzzy system construction using GA(genetic algorithm) and FCM(Fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. in the structure identification, input data is trans-formed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, the number of fuzzy rule is obtained by a given performance criterion. In the parameter identification, the premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this, one can systematically obtain optimal parameter and the v..