• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.28-35
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• 2002

The probabilistic seismic hazard analysis for engineering needs several active fault parameters as input data. Fault slip rates, the segmentation model for each fault, and the date of the most recent large earthquake in seismic hazard analysis are the critical pieces of information required to characterize behavior of the faults. Slip rates provide a basis for calculating earthquake recurrence intervals. Segmentation models define potential rupture lengths and are inputs to earthquake magnitude. The most recent event is used in time-dependent probability calculations. These data were assembled by expert source-characterization groups consisting of geologists, geophysicists, and seismologists evaluating the information available for earth fault. The procedures to prepare inputs for seismic hazard are illustrated with possible segmentation scenarios of capable fault models and the seismic hazards are evaluated to see the implication of considering capable faults models.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• Fashion & Textile Research Journal
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• v.8 no.4
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• pp.427-432
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• 2006

The purpose of this study was 1) to segment the market of inbound Japanese tourists based on the importance of tour activity that tourists perceived and 2) to examine the behavior of each segmentation purchasing cultural fashion items in Korea. Data were collected using a self-administered questionnaire survey in Seoul. Clustering analysis, Chisquare, and ANOVA test were used to conduct the data analysis on 288 out of 400 questionnaires. The inbound Japanese tourists market was segmented into 3 groups; culture oriented group, shopping oriented group, and multi-activity group. Three groups were significantly different in terms of age, income, purchase amount, purchase criteria, and degree of shopping satisfaction. Marketing strategies for segmented markets were discussed.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.439-454
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• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.245-252
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• 2012

The biggest difficulty the small and small business currently face is not to have the effective cusomer management system that is the computerization of management. This suggests their will to introduce data marketing in order to differentiate 'Customer Marketing' and 'One to One Marketing'. The potential needs as well as visible needs of customer should be considered in order to research and analyze the customer data. At this point mayor enterprises are paying much attention to Customer Segmentation and their related markets are expanding rapidly. I'll give a brief introduction to the Multi Criteria Importance-Performance Analysis and go into the problems that should be considered and which phase to emphasize when building this system.

• Journal of the Korean Home Economics Association
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• v.43 no.11 s.213
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• pp.185-196
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• 2005

The purpose of this research was to determine the relation between clothing evaluative criteria and segmented groups based on the perception of somatotype. The data for this research were collected from questionnaires of 192 females in Busan. Data were analyzed by frequency, factor analysis, cluster analysis, discriminant analysis, and regression analysis. Cluster analysis was used to identify groups of respondents based on the perception of somatotype difference factors. Based on the findings, three distinct groups were clustered: thin, moderate, fat. There were significant differences among the three groups in terms of clothing evaluative criteria. The result of regression analysis revealed that the perception of somatotype is a major determinant to influence the clothing evaluative criteria. The thin group preferred practical clothes while the fat group liked symbol clothes.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.127-135
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• 2023

Accurate segmentation of histopathology whole slide images (WSIs) is a crucial task for disease diagnosis and treatment planning. However, conventional automated segmentation algorithms may not always be applicable to WSI segmentation due to their large size and variations in tissue appearance, staining, and imaging conditions. Recent advances in interactive segmentation, which combines human expertise with algorithms, have shown promise to improve efficiency and accuracy in WSI segmentation but also presented us with challenging issues. In this paper, we propose a novel interactive segmentation method, ZoomISEG, that leverages multi-resolution WSIs. We demonstrate the efficacy and performance of the proposed method via comparison with conventional single-scale methods and an ablation study. The results confirm that the proposed method can reduce human interaction while achieving accuracy comparable to that of the brute-force approach using the highest-resolution data.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• Korean Circulation Journal
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• v.54 no.1
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• pp.30-39
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• 2024

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.