• Journal of the Korean association of regional geographers
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• v.20 no.4
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• pp.409-424
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• 2014

This study has analyzed the situation and space structure of Chungcheong Region using the variables of land prices. The total amount of land prices of Chungcheon Region was 950 trillion KRW as of December 2013, showing that it has increased by 6.46 times compared to 147 trillion KRW of 1980, which was the time of initial analysis. As a result of analyzing the dynamic changes in the region based on the land prices from 1980 through 2013, there were significant changes in the space structures only in the 1980s. The analysis of centrality based on the land prices of Chungcheong Region has shown that the center of Chungcheong Region was to the northeast of Sejong City in the 1980s and moved to the right with the rise in the land prices of Daejeon Region. The standard deviation of distance showed concentration around Daejeon entering the 1990s from the 1980s, and almost no concentration has been found after that.

• Korean Journal of Ichthyology
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• v.11 no.2
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• pp.163-171
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• 1999

The complete sequences of mtDNA control regions of six salmonines were determined: 1089 bp in lenok (Brachymystax lenok); 999 bp in cherry salmon (Oncorhynchus masou masou) and Ishikawa's cherry salmon (O. masou ishikauiae); 1002 bp in chum salmon (O. keta), and 1003 bp in rainbow trout (O. mykiss) and an albino mutant of rainbow trout. The estimated interspecific sequence divergences from PCR/direct sequencing data ranged from 5.42% to 16.49%. The organization of this region is similar to that of other vertebrates. A 81 bp tandemly repeated sequence, associated with length variation was observed in the 3' end of the salmonids control region in this study. In addition, The phylogenetic tree based on the control region sequences supported that cherry salmon was closer to chum salmon than to rainbow trout, while lenok was most distantly related species among six salmonines.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Steel and Composite Structures
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• v.19 no.1
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• pp.191-208
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• 2015

The sensitivity-based finite element model updating method has received increasing attention in damage detection of structures based on measured modal parameters. Finding an optimization technique with high efficiency and fast convergence is one of the key issues for model updating-based damage detection. A new simple and computationally efficient optimization algorithm is proposed and applied to damage detection by using finite element model updating. The proposed method combines the Gauss-Newton method with region truncation of each iterative step, in which not only the constraints are introduced instead of penalty functions, but also the searching steps are restricted in a controlled region. The developed algorithm is illustrated by a numerically simulated 25-bar truss structure, and the results have been compared and verified with those obtained from the trust region method. In order to investigate the reliability of the proposed method in damage detection of structures, the influence of the uncertainties coming from measured modal parameters on the statistical characteristics of detection result is investigated by Monte-Carlo simulation, and the probability of damage detection is estimated using the probabilistic method.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.283-286
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• 2001

In this paper, we present a robust region segmentation method based on the watershed transformation of a pseudo-distance map (PDM). A usual approach for the segmentation of a gray-scale image with the watershed algorithm is to apply it to a gradient magnitude image or the Euclidean distance map (EDM) of an edge image. However, it is well known that this approach suffers from the oversegmentation of the given image due to noisy gradients or spurious edges caused by a thresholding operation. In this paper we show thor applying the watershed algorithm to the EDM, which is a regularized version of the EDM and is directly computed form the edgestrength function (ESF) of the input image, significantly reduces the oversegmentation, and the final segmentation results obtained by a simple region-merging process are more reliable and less noisy than those of the gradient-or EDM-based methods. We also propose a simple and efficient region-merging criterion considering both boundary strengths and inner intensities of regions to be merged. The robustness of our method is proven by testing it with a variety of synthetic and real images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.63-71
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• 2003

This paper describes a color image segmentation algorithm based on region merging using hue difference as a restrictive condition. The proposed algorithm using mathematical morphology and a modified watershed algorithm does over-segmentation in the RGB space to preserve contour information of regions. Then, the segmentation result of color image is acquired by repeated region merging using hue differences as a restrictive condition. This stems from human visual system based on hue, saturation, and intensity. Hue difference between two regions is used as a restrictive condition for region merging because it becomes more important factor than color difference if intensity is not low. Simulation results show that the proposed color image segmentation algorithm provides efficient segmentation results with the predefined number of regions for various color images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.93-100
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• 2007

In this paper, a novel approach based on wavelet transform is proposed to process the scraped character which is represented on digital image. The basis idea is that the scraped character is described by its textured neighborhood, and it is decomposed into multiresolution features at different levels with its background region. The image is first decomposed into sub bands by applying Daubechies wavelets. Character features are extracted from the low frequency sub-bands by partition, FCM clustering and area-based region process. High frequency ones are activated by applying local energy density over a moving mask. Features are synthesized in order to reconstruct the original image state through inverse wavelet transform Background region is eliminated and character is extracted. The experimental results demonstrate the effectiveness of the proposed method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1587-1598
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• 2000

For the object-based video compression at very low bit rate, vieo segmentation is an essential part. In this paper, we propose a temporal video segmentation algorithms for motion pictures which is based on blocks. The algorithm is composed of three steps: (1) the change-detection, (2) the block merging, and (3) the block segmentation. The first step separates the change-detected region from background. Here, a new method for removing the uncovered region without motion estimation is presented. The second step, which is further divided into three substeps, estimates motions for the change-detected region and merges blocks with similar motions. The merging conditions for each substep as criteria are also given. The final step, the block segmentation, segments the boundary block that is excluded from the second step on a pixel basis. After describing our algorithm in detail, several experimental results along the processing order are shown step by step. The results demonstrate that the proposed algorithm removes the uncovered region effectively and produced objects that are segmented well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1391-1399
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• 2000

In this paper, we discussed the region-based image retrieval method using image segmentation. We proposed a segmentation method which can reduce the effect of a irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The content-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector. The similarity measure between regions is processed by the Euclidean distance of the feature vectors. The simulation results shows that the proposed method is reasonable.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.587-594
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• 2005

This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on Inか entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.