• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.63-70
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• 2012

In medical image processing field, object recognition is usually processed based on region segmentation algorithm. Region segmentation in the computing field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on R2-map information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2-map as seed points for 2D region growing and final boundary correction to enable region segmentation even when the border line was not clear. As a result, an average area difference of 7.5%, which was higher than the accuracy of conventional exist region segmentation algorithm, was obtained.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.705-716
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• 2002

This paper proposes a method which visualizes MRI head data in 3 dimensions with direct volume rendering. Though surface rendering is usually used for MRI data visualization, it has some limits of displaying little speckles because it loses the information of the speckles in the surfaces while acquiring the information. Direct volume rendering has ability of displaying little speckles, but it doesn't treat MRI data because of the data features of MRI. In this paper, we try to visualize MRI head data in 3 dimensions as follows. First, we separate the brain region from the head region of MRI head data, next increase the pixel level of the brain region, then combine the brain region with the increased pixel level and the head region without brain region, last visualizes the combined MRI head data with direct volume rendering. We segment the brain region from head region based on histogram threshold, morphology operations and snakes algorithm. The proposed segmentation method shows 91~95% similarity with a hand segmentation. The method rather clearly visualizes the organs of the head in 3 dimensions.

• Korean Journal of Computational Design and Engineering
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• v.4 no.2
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• pp.121-126
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• 1999

Existing data structures for non-manifold solid modelers use basic dat entities, such as vertex, edge, loop, face, shell, and region to find adjacency relationships. But, no one clearly identified what additional types of data entitles are necessary to represent incidence relationships. In this paper, we classified the boundary information of vertex, edge, face , and region from the 3-D space view. As the results we can clearly define the boundary information required for adjacency relationships. The existing B-rep data structures for solid modeler are compared whether they have the required boundary information.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.899-902
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• 2013

In this paper, we present a method to visualize the contact region between two molecules in a protein complex in a threedimensional space. The contact region of two molecules shows compatibility in geometric aspects. Usually, the computation of the area of contact region has been used to show the strength of compatibility. The numerical value and simple drawing of contact region would be useful for comparing the relative strength of different contacts, but it is not appropriate for analysing the geometric characteristics of the contact region. In this paper, we present a method to show the compatibility between two molecules by visualizing the distance information between them.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.37-43
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• 2014

In this paper, we propose a robust lip detection algorithm using color clustering. At first, we adopt AdaBoost algorithm to extract facial region and convert facial region into Lab color space. Because a and b components in Lab color space are known as that they could well express lip color and its complementary color, we use a and b component as the features for color clustering. The nearest neighbour clustering algorithm is applied to separate the skin region from the facial region and K-Means color clustering is applied to extract lip-candidate region. Then geometric characteristics are used to extract final lip region. The proposed algorithm can detect lip region robustly which has been shown by experimental results.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.927-930
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• 2000

Visual features of lip area play an important role in the visual speech information. We are concerned about correct lip area as region of interest (ROI). In this paper, we propose a robust and fast method for locating the mouth corners. Also, we define a region of interest at mouth during speech. A method, which we have used, only uses the horizontal and vertical image operators at mouth area. This searching is performed by fitting the ROI-template to image with illumination control. Most of the lip extraction algorithms are dependent on luminosity of image. We just used the binary image where the variable threshold is applied. The variable threshold varies to illumination condition. In order to control those variations, the gray-tone is converted to binary image by threshold, which is obtained through Multiple Linear Regression Analysis (MLRA) about divided 2D special region. Thus we obtained the region of interest at mouth area, which is the robust extraction about illumination. A region of interest is automatically extracted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.866-872
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• 2004

In fingerprint verification and identification, fingerprint and background region should be segmented. For this purpose, most systems obtain variance of brightness of X and Y direction using Sobel mask. To decide given local region is background or not, the variance is compared with a certain threshold. Although this method is simple, most fingerprint image does not separated with two region of fingerprint and background region. In this paper, we presented a new segmentation algorithm based on run-length connectivity analysis. For a given binary image after thresholding, suggested algorithm calculates RL of X and Y direction. Until the given image is segmented to two regions, small run region is successively inverted. Experimental result show that this algorithm effectively separates fingerprint region and background region.

• Journal of National Security and Military Science
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• s.9
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• pp.191-206
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• 2011

This study is presented a scheme that information security research institutions located within the central area can be participated actively m national defense information security industry. The many of information security company are located in the central region(Daejeon) and there are many research institutes. However, the participation rate of the Defense Information Security Industry is not high compared to other provinces. Although a variety of reasons, there are no the Defense Privacy Office that could have a role in protocol and the information about the industry. In addition, the Department of Defense related to national defense information security industry have not information about research institutions in the central region and are not well to identify the characteristics of institutional technology and research. So in this paper we presented some of the alternatives. 1) Building Pool involved in information security research according to the characteristics of each agency 2) Constitute the research community between Research institutions and the company 3) Build the technology cooperation between research institutions and the defense research institutes 4) Utilization of industry/university/research institutes related to Information Security Industry 5) Make strategic alliances among research institutes based on technical expertise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.575-583
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• 2002

JBIG2 is an International Standard fur compression of Bi-level images and documents. JBIG2 supports three encoding modes for high compression according to region features of documents. One of which is generic region coding for bitmap coding. The basic bitmap coder is either MMR or arithmetic coding. Pattern matching coding method is used for text region, and halftone pattern coding is used for halftone region. In this paper, a document is segmented into line-art, halftone and text region for JBIG2 encoding and JBIG2 CODEC is implemented. For efficient region segmentation of documents, region segmentation method using wavelet coefficient is applied with existing boundary extraction technique. In case of facsimile test image(IEEE-167a), there is improvement in compression ratio of about 2% and enhancement of subjective quality. Also, we propose arbitrary shape halftone region coding, which improves subjective quality in talc neighboring text of halftone region.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.19-27
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• 2010

An region of interest processing technique is to handle preferentially some part of an image dynamically according to region of interest of the users in JPEG2000 image. A small image is not important, but in a big image the specified region that the user indicated has to be handled preferentially because it takes long time to display the whole image. If the user indicates a region of the outline image, the browser masks the region and sends the mask information to the source that transmitted the image. The server which got the mask information preferentially sends the code blocks matching the masks. Here, quickly generating mask information is important, so, in this paper using predefined 48 mask patterns, selecting one of the patterns according to the distribution of ROI(Region-of-Interest) and background, we remarkably reduced the time computing the mask region. Blocks that the patterns are applied are the blocks mixed of ROI and background in a block. If a whole block is an ROI or a background, these patterns are not applied. As results, comparing to the method that precisely handles ROI and background, the quality is unsatisfactory but the processing time remarkably reduced.