• 한국전산유체공학회:학술대회논문집
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• 2001.05a
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• pp.127-133
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• 2001

In this paper, the preconditioned multistage time stepping methods which are popular multigrid smoothers is studied for the compressible flow calculations. Fourier analysis on the local time stepping and block-Jacobi preconditioned residual operators is performed using the linearized 2-D Navier-Stokes equations. It fumed out that block-Jacobi preconditioner has better performance in eigenvalue clustering. They are implemented in the 2-D compressible Euler and Wavier-Stokes calculations with multigrid methods to verify that the block-Jacobi preconditioned multistage time stepping shows better performance in convergence acceleration.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.1-12
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• 2007

To use multimedia data in restricted resources of mobile environment, any management method of MPEG-7 documents is needed. At this time, some XML clustering methods can be used. But, to improve the performance efficiency better, a new clustering method which uses the characteristics of MPEG-7 documents is needed. A new clustering improved query processing speed at multimedia search and it possible document storage about various application suitably. In this paper, we suggest a new clustering method of MPEG-7 documents for effective management in multimedia data of large capacity, which uses some semantic relationships among elements of MPEG-7 documents. And also we compared it to the existed clustering methods.

• Journal of the Korean housing association
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• v.19 no.6
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• pp.85-93
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• 2008

This study analyzes areas, traffic lines and characteristics of block of traditional villages in order to suggest how to build urban village in the way that can solve problems occurring in residential areas these days. The study showed the following results: 1) Traditional villages have definite boundary and entrance, and the community area for the villages is close to the entrance to encourage community activities of villagers. 2) With an access in the form of a blind alley branched from the main road, traditional villages form a small-sized clustering and encourage community activities in a natural way. 3) Formed of block with a pattern of net, blind alley or standing in a line on both sides, traditional villages help residents to form close relations between. These findings suggest that for building desirable urban villages, 1) they should have definite boundary, 2) size and location of community area should be determined in the way to activate community activities of residents, 3) roads inside the village should have branched form rather than standardized check pattern so that small-sized clustering could be formed along the branched inner roads, and 4) clustering in villages should be arranged in a line on both sides or in the form of a blind alley giving consideration to the length and width of roads. The roads should be also of a closed type so that residents could create strong bonds with their neighbors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.813-829
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• 2022

In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem. In the existing works, the unified representation is usually constructed in the original data space. However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss. To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain. In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency. In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function. Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.195-198
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• 2000

Fractal image compression can reduce the size of image data by contractive mapping of original image. The mapping is affine transformation to find the block(called range block) which is the most similar to the original image. Fractal is very efficient way to reduce the data size. However, it has high distortion rate and requires long encoding time. In this paper, we present the simulation result of fractal and VQ hybrid systems which use different clustering algorithms, normal and improved competitive learning SOFM. The simulation results showed that the VQ hybrid fractal using improved competitive learning SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.81-84
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• 2000

Fractal image compression can reduce the size of image data by contractive mapping of original image. The mapping is affine transformation to find the block(called range block) which is the most similar to the original image. Fractal is very efficient way to reduce the data size. However, it has high distortion rate and requires long encoding time. In this paper, we present the simulation result of fractal and VQ hybrid systems which use different clustering algorithms, normal and improved competitive learning SOFM. The simulation results showed that the VQ hybrid fractal using improved competitive learning SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

• International Journal of Contents
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• v.9 no.1
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• pp.6-10
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• 2013

Foreground estimation in object segmentation has been an important issue for last few decades. In this paper we propose a GrabCut based automatic foreground estimation method using block clustering. GrabCut is one of popular algorithms for image segmentation in 2D image. However GrabCut is semi-automatic algorithm. So it requires the user input a rough boundary for foreground and background. Typically, the user draws a rectangle around the object of interest manually. The goal of proposed method is to generate an initial rectangle automatically. In order to create initial rectangle, we use Gabor filter and Saliency map and then we use 4 features (amount of area, variance, amount of class with boundary area, amount of class with saliency map) to categorize foreground and background. From the experimental results, our proposed algorithm can achieve satisfactory accuracy in object segmentation without any prior information by the user.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.911-921
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• 2004

Accurate minutiae extraction from input fingerprint images is one of the critical modules in robust automatic fingerprint identification system. However, the performance of a minutiae extraction is heavily dependent on the quality of the input fingerprint images. If the preprocessing is performed according to the fingerprint image characteristics in the image enhancement step, the system performance will be more robust. In this paper, we propose a knowledge-based preprocessing method, which extracts S features (the mean and variance of gray values, block directional difference, orientation change level, and ridge-valley thickness ratio) from the fingerprint images and analyzes image quality with Ward's clustering algorithm, and enhances the images with respect to oily/neutral/dry characteristics. Experimental results using NIST DB 4 and Inha University DB show that clustering algorithm distinguishes the image Quality characteristics well. In addition, the performance of the proposed method is assessed using quality index and block directional difference. The results indicate that the proposed method improves both the quality index and block directional difference.

• Journal of information and communication convergence engineering
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• v.16 no.4
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• pp.258-263
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• 2018

Color Doppler sonography is a useful tool for examining blood flow and related indices. However, it should be done by well-trained operator, that is, operator subjectivity exists. In this paper, we propose an automatic blood flow area extraction method from brachial artery that would be an essential building block of computer aided color Doppler analyzer. Specifically, our concern is to examine hypertension suspicious (prehypertension) patients who might develop their symptoms to established hypertension in the future. The proposed method uses fuzzy C-means clustering as quantization engine with careful seeding of the number of clusters from histogram analysis. The experiment verifies that the proposed method is feasible in that the successful extraction rates are 96% (successful in 48 out of 50 test cases) and demonstrated better performance than K-means based method in specificity and sensitivity analysis but the proposed method should be further refined as the retrospective analysis pointed out.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.124-132
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• 2014

In this paper, we present a novel backlit region detection and contrast enhancement method using fuzzy C-means clustering and adaptively partitioned block based contrast stretching. The proposed method separates an image into both dark backlit and bright background regions using adaptively partitioned blocks based on the optimal threshold value computed by fuzzy logic. The detected block-wise backlit region is refined using the guided filter for removing block artifacts. Contrast stretching algorithm is then applied to adaptively enhance the detected backlit region. Experimental results show that the proposed method can successfully detect the backlit region without a complicated segmentation algorithm and enhance the object information in the backlit region.