• Journal of the Korean Society of Costume
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• v.38
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• pp.193-211
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• 1998

Without a solid marketing system in placed, the fashion industry cannot flourish on out-standing design or technology alone. Even though the significance of collecting and analyzing information, merchandising, and retail distribution is recognized, these functions are not firmly rooted or prevalent in our industry. In contrast, Italy which possesses similar demographic traits such as the lack of natural resources and other physical factors has succeed-ed in globalizing its fashion market by responding swiftly and exercising flexiblity to its constantly changing consumer demand. This in turn has earned Italy the competitive edge in the global fashion arena. Italy's unique management skills and operation know-how, along with successful market strategies come into play in bringing competitiveness to Italy's fashion market. Firstly, smaller companies with ability to adopt swiftly to the ever changing market. Secondly, fashion friendly social environment. Thirdly, niche marketing through highly specialized system and differentiation. Fourthly, timeless innovation through intense corporate competition. Lastly, establishment of foundations to support the industry through diverse networking. The alone building blocks have formed a basis for erecting an unparalleled market with a reputation for excellence in design and quality in the global fashion world. This study has examined how Italy's fashion industry has evolved from an underdeveloped textile business into a cutting edge fashion in-dustry. Italy's unique business processes and practices were studied to come up with a collection and merchandising ideas in a niche market. By selecting this venue we are able to continuously grow and develop in a market with diverse consumer needs. To analyze the Italian fashion market, data from 3 institutions were utilized, namely, CIT-ER which has provided consumer trends and sales analysis, SITA,a data service provided statistics from the textile and apparel businesses, and NBI has also furnished valuable data. Italian consumer preference, buying behavior, consumer profile, retail channels and other related data from the above institutions has formed a backbone for market segmentation and target markets, and as a result, we were able to zero in on the type of consumer, produce, pricing and retail channels for our womenswear. Going forward the direction is to elevate product image and pretige, and create syn-ergy between related industries, and at the same note, in order to develop internationally recognized brands such as Max Mara and Benetton. Certain elements such as the specialization of the fashion industry, alon-g with fashion-related data base and systems support, and most importantly experts with acute fashion sense and capacity to analyze pertinent data are in need. I firmly believe that we can achieve Italy's level in the fashion market with support from the government and unrelenting effort within the industry itself, and hope that this report can prove to be useful.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.93-102
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• 1996

For an atomatic generation of DEM(Digital Elevation Model) by computer, it is a time-consumed work to determine adquate matches from stereo images. Correlation and evenly distributed area-based method is generally used for matching operation. In this paper, we propose a new approach that computes matches efficiantly by changing the size of mask window and search area according to the given terrain information. For image segmentation, at first edge-preserving smoothing filter is used for preprocessing, and then region growing algorithm is applied for the filterd images. The segmented regions are classifed into mountain, plain and water area by using MRF(Markov Random Filed) model. Maching is composed of predicting parallex and fine matching. Predicted parallex determines the location of search area in fine matching stage. The size of search area and mask window is determined by terrain information for each pixel. The execution time of matching is reduced by lessening the size of search area in the case of plain and water. For the experiments, four images which are covered $10km{\times}10km(1024{\times}1024\;pixel)$ of Taejeon-Kumsan in each are studied. The result of this study shows that the computing time of the proposed method using terrain information for matching operation can be reduced from 25% to 35%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.107-115
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• 2011

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.132-139
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• 2015

Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot's location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.416-421
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• 2010

The KISTI (Korea Institute of Science and Technology Information) began to produce the Korean human information called Visible Korean and Digital Korean since 2000 because there was no human information in Korea which could represent the physical characteristics of Korean human body. The Visible Korean consists of CT, MR, sectioned and segmented images of Korean human body. We obtained the serially sectioned images by grinding the Korean cadaver in horizontal direction and segmented these images by outlining the inner organs of human. We have produced the sectioned images of Korean male whole body, male head, and female pelvis in2008. The segmentation and 3D reconstruction of these images are now in proceeding. The Digital Korean consists of CT images of about 100 Korean cadavers. These CT images were segmented by individual bone, reconstructed to produce the 3D bone models and the skin surface model was also added. The mechanical properties of individual bones were obtained by measuring the property of individual bone sample. We have distributed these Korean human informations to users in domestic and abroad. About 70 institutes in domestic, and 20 institutes in abroad have used our data in research use and nearly 160 proceedings and articles were published since 2001. We think these human informations have a role of medical information infrastructure that could be used in the field of medical education, biomechanics, virtual reality etc.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.35 no.3
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• pp.167-173
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• 2013

Purpose: The aim of this study is to analyze the series of panoramic radiograph of implant patients using the system to measure peri-implant crestal bone loss according to the elapsed time from fixture installation time to more than three years. Methods: Choose 10 patients having 45 implant fixtures installed, which have series of panoramic radiograph in the period to be analyzed by the system. Then, calculated the crestal bone depth and statistics and selected the implant in concerned by clicking the implant of image shown on the monitor by the implemented pattern recognition system. Then, the system recognized the x, y coordination of the implant and peri-implant alveolar crest, and calculated the distance between the approximated line of implant fixture and alveolar crest. By applying pattern recognition to periodic panoramic radiographs, we attained the results and made a comparison with the results of preceded articles concerning peri-implant marginal bone loss. Analyzing peri-implant crestal bone loss in a regression analysis periodic filmed panoramic radiograph, logarithmic approximation had highest $R^2$ value, and the equation is as shown below. $y=0.245Logx{\pm}0.42$, $R^2=0.53$, unit: month (x), mm (y) Results: Panoramic radiograph is a more wide-scoped view compared with the periapical radiograph in the same resolution. Therefore, there was not enough information in the radiograph in local area. Anterior portion of many radiographs was out of the focal trough and blurred precluding the accurate recognition by the system, and many implants were overlapped with the adjacent structures, in which the alveolar crest was impossible to find. Conclusion: Considering the earlier objective and error, we expect better results from an analysis of periapical radiograph than panoramic radiograph. Implementing additional function, we expect high extensibility of pattern recognition system as a diagnostic tool to evaluate implant-bone integration, calculate length from fixture to inferior alveolar nerve, and from fixture to base of the maxillary sinus.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.25-33
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• 2015

The study of ecosystem assessment(ES) is based on land cover information, and primarily it is performed at the global scale. However, these results as data for decision making have a limitation at the aspects of range and scale to solve the regional issue. Although the Ministry of Environment provides available land cover data at the regional scale, it is also restricted in use due to the intrinsic limitation of on screen digitizing method and temporal and spatial difference. This study of objective is to generate UAV land cover map. In order to classify the imagery, we have performed resampling at 5m resolution using UAV imagery. The results of object-based image segmentation showed that scale 20 and merge 34 were the optimum weight values for UAV imagery. In the case of RapidEye imagery;we found that the weight values;scale 30 and merge 30 were the most appropriate at the level of land cover classes for sub-category. We generated land cover imagery using example-based classification method and analyzed the accuracy using stratified random sampling. The results show that the overall accuracies of RapidEye and UAV classification imagery are each 90% and 91%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.141-146
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• 2017

New infectious diseases such as MERS have been in need of many measures such as initial discovery, isolation, and crisis response. In addition, the culture of hospitals is changing, such as the general public 's visiting and Nursing Care Integration Services. However, as the qualifications and regulations of medical personnel in hospitals become rigid, overseas such as linens, wastes movements are replacing possible works with robots. we have developed a hospital logistics robot that can carry out various goods delivery within a hospital, and can move various kinds of objects safely to a desired location. In this thesis, we have studied a hospital logistics robot that can carry out various kinds of goods delivery within the hospital, and can move various kinds of objects such as waste, and linen safely to a desired location. The movement of a robot in a hospital may cause a collision between a person and an object, so that the collision must be prevented. In order to prevent collision, it is necessary to recognize whether or not an object exists in the movement path of the robot. And if there is an object, it should recognize whether it moves or not. In order to recognize human beings and objects, we recognize the person with face/body recognition technology and generate the context awareness of the object using 3D Vision image segmentation technology. We use the generated information to create a map that considers objects and person in the robot moving range. Thus, the robot can be operated safely and efficiently.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.163-173
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• 2014

When evaluating the spatial resolution images and evaluation of low contrast resolution using CT standard phantom, and might present a automated quantitative evaluation method for minimizing errors by subjective judgment of the evaluator be, and try to evaluate the usefulness. 120kVp and 250mAs, 10mm collimation, SFOV(scan field of view) of 25cm or more than, exposure conditions DFOV(display field of view) of 25cm, and were evaluated the 24 passing images and 20 failing images taken using a standard reconstruction algorithm by using the Nuclear Associates, Inc. AAPM CT Performance Phantom(Model 76-410). Quantitative evaluation of low contrast resolution and spatial resolution was using an evaluation program that was self-developed using the company Mathwork Matlab(Ver. 7.6. (R2008a)) software. In this study, the results were evaluated using the evaluation program that was self-developed in the evaluation of images using CT standard phantom, it was possible to evaluate an objective numerical qualitative evaluation item. First, if the contrast resolution, if EI is 0.50, 0.51, 0.52, 0.53, as a result of evaluating quantitatively the results were evaluated qualitatively match. Second, if CNR is -0.0018~-0.0010, as a result of evaluating quantitatively the results were evaluated qualitatively match. Third, if the spatial resolution, as a result of using a image segmentation technique, and automatically extract the contour boundary of the hole, as a result of evaluating quantitatively the results were evaluated qualitatively match.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.