• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.41-44
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• 2003

First, we investigate some properties of fuzzy compactness. Second, we introduce the concept of fuzzy local compactness in fuzzy topological space and study some of its properties. Finally, we investigate some relations between F-compactness in fuzzy topological spaces and one in fuzzy hyperspaces.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.153-158
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• 2002

In this paper we introduce the concept of a fuzzy almost c-continuity and investigate some of its properties.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.285-288
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• 2002

In this paper, we introduce the concept of a fuzzy almost c-continuity and investigate some of its properties.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.368-374
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• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.323-328
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• 2004

The development of group-specific tissue probability maps (TPM) provides a priori knowledge for better result of cerebral tissue classification with regard to the inter-ethnic differences of inter-subject variability. We present sequential procedures of group-specific TPM and evaluate the age effects in the structural differences of TPM. We investigated 100 healthy volunteers with high resolution MRI scalming. The subjects were classified into young (60, 25.92+4.58) and old groups (40, 58.83${\pm}$8.10) according to the age. To avoid any bias from random selected single subject and improve registration robustness, average atlas as target for TPM was constructed from skull-stripped whole data using linear and nonlinear registration of AIR. Each subject was segmented into binary images of gray matter, white matter, and cerebrospinal fluid using fuzzy clustering and normalized into the space of average atlas. The probability images were the means of these binary images, and contained values in the range of zero to one. A TPM of a given tissue is a spatial probability distribution representing a certain subject population. In the spatial distribution of tissue probability according to the threshold of probability, the old group exhibited enlarged ventricles and overall GM atrophy as age-specific changes, compared to the young group. Our results are generally consistent with the few published studies on age differences in the brain morphology. The more similar the morphology of the subject is to the average of the population represented by the TPM, the better the entire classification procedure should work. Therefore, we suggest that group-specific TPM should be used as a priori information for the cerebral tissue classification.