• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.2
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• pp.137-146
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• 1990

A series of images whose sized and resolutions differ by a constant factor are called an image pyramid. Because the images at high levels are small, large object can be detected on high levels of the pyramid at low cost, But in this way, the boundaries of objects are not accurately localized. Therefore the pyramid algorithms extracte the objects by segmentation the constructed image using bottom-up method and description it in an original resolution using inverse bottom-up method. In this paper, we can project an object down to the next lower level of the pyramid and apply to the boundary adjustment algorithm at that level to localize it more precisely. We repeat the process at successively lower levels. In this paper, we present a method of boundary adjustment using an image pyramid to obtain optimal boundary. The performance of the proposed algorithm is compared to those of the conventional method in term of subjective quality of object boundary.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.344-356
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• 2001

This paper deals with the subsequence searching problem under time-warping in sequence databases. Our work is motivated by the observation that subsequence searches slow down quadratically as the average length of data sequences increases. To resolve this problem, the Segment-Based Approach for Subsequence Searches (SBSS) is proposed. The SBASS divides data and query sequences into a series of segments, and retrieves all data subsequences that satisfy the two conditions: (1) the number of segments is the same as the number of segments in a query sequence, and (2) the distance of every segment pair is less than or equal to a tolerance. Our segmentation scheme allows segments to have different lengths; thus we employ the time warping distance as a similarity measure for each segment pair. For efficient retrieval of similar subsequences, we extract feature vectors from all data segments exploiting their monotonically changing properties, and build a spatial index using feature vectors. Using this index, queries are processed with the four steps: (1) R-tree filtering, (2) feature filtering, (3) successor filtering, and (4) post-processing. The effectiveness of our approach is verified through extensive experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.1-10
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• 2022

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of the Korea Computer Graphics Society
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• v.23 no.1
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• pp.49-55
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• 2017

Computed tomographic angiography (CTA) is widely used in the diagnosis and treatment of coronary artery disease because it shows not only the whole anatomical structure of the cardiovascular three-dimensionally but also provides information on the lesion and type of plaque. However, due to the large size of the image, there is a limitation in manually extracting coronary arteries, and related researches are performed to automatically extract coronary arteries accurately. As the coronary artery originate from the ascending aorta, the ascending aorta and ostium should be detected to extract the coronary tree accurately. In this paper, we propose an automatic segmentation for the ostium as a starting structure of coronary artery in CTA. First, the region of the ascending aorta is initially detected by using Hough circle transform based on the relative position and size of the ascending aorta. Second, the volume of interest is defined to reduce the search range based on the initial area. Third, the refined ascending aorta is segmented by using a two-dimensional geodesic active contour. Finally, the two ostia are detected within the region of the refined ascending aorta. For the evaluation of our method, we measured the Euclidean distance between the result and the ground truths annotated manually by medical experts in 20 CTA images. The experimental results showed that the ostia were accurately detected.

• Korea Journal of Hospital Management
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• v.10 no.1
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• pp.93-114
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• 2005

The prenatal care is the preventive medical service to help the pregnant mother deliver the healthy baby. It's regular examines give some chances to check-up the healthy conditions. This thesis concentrates on the CRM system to support an effective prenatal care system and prove the effectiveness of it. As CRM is the adapted management related to the customer's own information, it is important to develop the CRM model classified by the patients characteristics. A general hospital in Busan operated the CRM system to carry out the effective prenatal care and there is an analysis to ensure the effectiveness of CRM system for the pregnant women in our maternity ward. The results can be summarized as follows: 1) According to the comparisons with the CRM system, we can conclude the system is desirable. (1) Maternal Age : In the age distribution, the prenatal visit frequency, triple marker freqency, oral GTT and targeted ultrasonography in the experimental group in 30 to 34 years old is higher on the whole. For over 35 years old group, the higher frequency comes out in the oral GTT and targeted ultrasonography and for 25 to 29 years old group the different figure shows just in the targeted ultrasonography. (2) Area of residence: There is a clear difference in all the items in Busan and near area but no sign of difference in prenatal visits and oral GTT in other residencial area. Especially in the targeted ultrasonography the higher figure shows in the experimental group located in the both areas. The targeted ultrasonography is known as the specific examination which should be examined by the specialists, on the contrary the other examinations can be operated in the small clinic. So the public information and seminars related with ultrasonography increases the check-up frequency. The clinic requests some ultrasonographical examinations to the specialists in general hospital. (3) Parity: The clear difference shows that the CRM system causes the prenatal visit frequency to become higher in experimental group. The figure is 9.7 times and 8.6 times each. This is opposite that the past study said multiparity reduced the average prenatal visits. But the result of CRM is considered as the method to help the multiparity understand the importance of the prenatal care. (4) Obstetrical history: In the experimental group of the spontaneous delivery group, the figure is higher in the prenatal visit frequency, triple marker, oral GTT and targeted ultrasonography but the Caesarean section delivery group has higher figure in targeted ultrasonography. (5) In the first check-up, the rate of targeted ultrasonography in under 16 week pregnancy, in the 16 week pregnancy to 32 week pregnancy and the over 32 week pregnancy in the experimental group is upper than the compared one. For the oral GTT, there is a difference in under 16 week pregnancy but no difference in prenatal visits and triple marker. 2) The analysis of characteristics of prenatal care through the decision tree resulted in the fact that the most important variable is the residential area. After the delivery frequency is following, the obstetrical history and maternal age are in order. It is the same result in the triple marker and oral GTT. Consequently it is the same order of important variables in CRM system. The effectiveness of CRM system is proved in this study. The CRM system is a marketing method to control and lead the customers through the segmentation of customer data. It increases the new customer aquisition, maintenance of loyal customers, augmentation of customers value, activation of potential customers and creation of life time customers. So eventually it can enlarge the customers value. The medical institution should make efforts to establish the data base enforced by the customer's information on the underlying ordinary data system to carry out the CRM system effectively. In addition, it should develop the a variety of marketing strategy in order to set up one to one marketing satisfying the needs of individual patients.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.