• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.385-392
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• 2016

Market analysis for a business plain is required for the success in the modern world. Most important part in this analysis is pedestrian traffic counting. A traditional way for this is counting it in person. However, it causes high labor costs and mistakes. This paper proposes an automatic algorithm to measure the pedestrian traffic count using images with webcam. The proposed algorithm is composed of two parts: pedestrian area detection and movement tracking. In pedestrian area detection, moving blobs are extracted and pedestrian areas are detected using HoG features and Adaboost algorithm. In movement tracking, multi-level matching and false positive removal are applied to track pedestrian areas and count the pedestrian traffic. Multi-level matching is composed of 3 steps: (1) the similarity calculation between HoG area, (2) the similarity calculation of the estimated position with Kalman filtering, and (3) the similarity calculation of moving blobs in the pedestrian area detection. False positive removal is to remove invalid pedestrian area. To analyze the performance of the proposed algorithm, a comparison is performed with the previous human area detection and tracking algorithm. The proposed algorithm achieves 83.6% accuracy in the pedestrian traffic counting, which is better than the previous algorithm over 11%.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.329-334
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• 2014

In this study, a new method that automatically segments trabecular bone for its morphological analysis using micro-computed tomography imaging was proposed. In the proposed method, the bone region was extracted using a threshold value, and the outer boundary of the bone was detected. The sphere of maximum size with the corresponding voxel as the center was obtained by applying the sphere-fitting method to each voxel of the bone region. If this sphere includes the outer boundary of the bone, the voxels included in the sphere are classified as cortical bone; otherwise, they are classified as trabecular bone. The proposed method was applied to images of the distal femurs of 15 mice, and comparative experiments, with results manually divided by a person, were performed. Four morphological parameters-BV/TV, Tb.Th, Tb.Sp, and Tb.N-for the segmented trabecular bone were measured. The results were compared by regression analysis and the Bland-Altman method; BV/TV, Tb.Th, Tb.Sp, and Tb.N were all in the credible range. In addition, not only can the sphere-fitting method be simply implemented, but trabecular bone can also be divided precisely by using the three-dimensional information.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.377-382
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• 2018

This study deals with internal state and tissue density analysis methods for red ginseng grade determination. For internal measurement of red ginseng, there have been various studies on nondestructive testing methods since the 1990s, It was difficult to grasp the most important inner hole and inside whites in the grading. So in this study, we developed a closed capturing device for infra-red illumination environment, and developed an internal measurement system that can detect the presence and diameter of inner hole and inside whites. Made devices consisted of infrared lights with a high transmission rate of red ginseng in 920 nanometer wave band, a infra-red camera and a Y axis actuator with a red ginseng automatically controlled focus on the camera. The proposed algorithm performs an auto-focus system on the Y-axis actuator to automatically adjust the sharp focus of the object according to the size and thickness. Then red ginseng is rotated $360^{\circ}$ at $1^{\circ}$ intervals and 360 total images are acquired, and reconstructed as a sinogram through Radon transform and Back-projection algorithm was performed to acquire internal images of red ginseng. As a result of the algorithm, it was possible to acquire internal cross-sectional image regardless of the thickness and shape of red ginseng. In the future, if more than 10,000 different shapes and sizes of red ginseng internal cross-sectional image are acquired and the classification criterion is applied, it can be used as a reliable automated ginseng grade automatic measurement method.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.507-516
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• 2019

Costs invested in road maintenance and road development are on the rise. However, due to accidents such as portholes and ground subsidence, the risks to the drivers' safety and the material damage caused by accidents are also increasing. Following this trend, we have developed a system that determines road damage, according to the magnitude of vibration generated without directly intervening the driver when driving. In this paper, we implemented the system using a remote control car (RC car) simulator due to the limitation of the environment in which the actual vehicle is not available in the process of developing the system. In addition, we attached a vibration sensor and GPS sensor to the body of the RC car simulator to measure the vibration value and location information generated by the movement of the vehicle in real-time while driving, and transmitting the corresponding data to the server. In this way, we implemented a system that allows external users to check the damage of roads and the maintenance of the repaired roads based on data more easily than the existing systems. By using this system, we can perform early prediction of road breakage and pattern prediction based on the data. Further, for the RC car simulator, commercialization will be possible by combining it with business in other fields that require flatness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1289-1298
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• 2014

Pavement crack sealing method, which is one of the methods to maintain and repair the road, prevents the extending of cracks by repairing cracks in its early occurrence and has already been applied to many roadworks in advanced foreign country for a long time. But in the conventional crack sealing method, traffic accidents occur frequently during the repair because it's commonly performed on the heavy traffic road or highway. It also has some difficulties in securing the safety of workers from the risk of burns caused by heated sealant. In an effort to solve these problems, automated pavement crack sealing machines such as ARMM, OCCSM, TTLS have been developed in advanced foreign country since early 1990s. Also APCS in 2004 and ACSTM in 2013 were already developed domestically. However, since these automated crack sealers developed from a number of research institutions have different test-bed conditions and productivity measurement models, it's difficult to compare and evaluate them objectively. In this study, the image processing time of the respective machines and the movement time of each motion on the work process were estimated by using fully autonomous mapping and semi-automatic mapping in order to measure the productivity in the same environmental conditions. In addition, the productivity measurement test-bed reflected domestic road characteristics was designed to estimate and compare the productivity of the automated crack sealing machines.

• Spatial Information Research
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• v.21 no.1
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• pp.63-73
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• 2013

Geographic features are reflected in satellite images, which contain characteristic elements. Information on changes can be obtained through a comparison of images taken at different times. If multi-temporal images can be classified through the use of an unsupervised method, this is likely to improve the accuracy of image classification and contribute to various applications. A rule-based image classification algorithm for automatic processing without human involvement has been developed, but it must be verified that its results are not affected by imperfect elements. In this study, Landsat images of Jeju Island were used to carry out a rule-based image classification. The application results were examined for complex cases, including the presence of clouds in the images, different photographed times, and the type of target area, such as city, mountain, or field. The presence of clouds did not affect calculations, and appropriate classification rules were applied, depending on the different photographed times. The expansion of the urban areas of Jeju and the increase of facilities such as vinyl greenhouses in Seoguipo were identified. Furthermore, space information changes and accurate classifications for Jeju Island were obtained. With the goal of performing high-quality unsupervised classifications, measures to generalize and improve the methods employed were searched for. The findings of this study could be used in time-series analyses of images for various applications, including urban development and environmental change monitoring.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.13-22
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• 2008

The objective of this study is to evaluate the applicability of extracted PRMS input parameters by KGIS-Hydrology over Yongdam-Dam watershed. KGIS-Hydrology is a system for automatic extraction and analysis of watershed characteristic data. Input parameters of PRMS were generated from GIS data (DEM, soil, forest type, etc.) using KGIS-Hydrology. Multi-temporal meteorological data from Jangsu station of KMA (Korea Meteorological Administration) were used for all simulation periods. Input parameters of PRMS were optimized using observed runoff data of Yongdam-Dam station (1966-2001) and validated using observed runoff data of Yongdam-Dam station (2002-2006, Yongdam-Dam watershed). The results showed that the simulated flows were much closed to the observed flows of Yongdam-Dam (2002-2006) and Donghyang (2001-2004) station by 0.49~0.83 and 0.57~0.75 model efficiencies, respectively.

• Management & Information Systems Review
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• v.36 no.4
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• pp.129-148
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• 2017

Conventional exhibition management information systems recommend tourist attractions that are close to the place in which an exhibition is held. Some recommended attractions by the location-based recommendation could be meaningless when nothing is related to the exhibition's topic. Our goal is to recommend attractions that are related to the content presented in the exhibition, which can be coined as content-based recommendation. Even though human exhibition curators can do this, the quality is limited to their manual task and knowledge. We propose an automatic way of discovering attractions relevant to an exhibition of interests. Language resources are incorporated to discover attractions that are more meaningful. Because a typical single machine is unable to deal with such large-scale language resources efficiently, we implemented the algorithm on top of Apache Spark, which is a well-known distributed computing framework. As a user interface prototype, a web-based system is implemented that provides users with a list of relevant attractions when users are browsing exhibition information, available at http://bike.snu.ac.kr/WARP. We carried out a case study based on Korean Tourism Organization Linked Open Data with Korean Wikipedia as a language resource. Experimental results are demonstrated to show the efficiency and effectiveness of the proposed system. The effectiveness was evaluated against well-known exhibitions. It is expected that the proposed approach will contribute to the development of both exhibition and tourist industries by motivating exhibition visitors to become active tourists.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.15-22
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• 2002

Image segmentation methods partition an image into meaningful regions. For image composition and analysis, it is desirable for the partitioned regions to represent meaningful objects in terms of human perception and manipulation. Despite the recent progress in image understanding, however, most of the segmentation methods mainly employ low-level image features and it is still highly challenging to automatically segment an image based on high-level meaning suitable for human interpretation. The concept of HCI (Human Computer Interaction) can be applied to operator-assisted image segmentation in a manner that a human operator provides guidance to automatic image processing by interactively supplying critical information about object boundaries. Intelligent Scissors and Snakes have demonstrated the effectiveness of human-assisted segmentation [2] [1]. This paper presents a method for interactive image segmentation for more efficient and effective detection and tracking of object boundaries. The presented method is partly based on the concept of Intelligent Scissors, but employs the well-established Canny edge detector for stable edge detection. It also uses "sewing method" for including weak edges in object boundaries, and 5-direction search to promote more efficient and stable linking of neighboring edges than the previous methods.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.455-462
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• 2001

The World Wide Web has become one of the major services provided through Internet. When searching the vast web space, users use bookmarking facilities to record the sites of interests encountered during the course of navigation. One of the typical problems arising from bookmarking is that the list of bookmarks lose coherent organization when the the becomes too lengthy, thus ceasing to function as a practical finding aid. In order to maintain the bookmark file in an efficient, organized manner, the user has to classify all the bookmarks newly added to the file, and update the folders. This paper introduces our learning agent called BClassifier that automatically classifies bookmarks by analyzing the contents of the corresponding web documents. The chief source for the training examples are the bookmarks already classified into several bookmark folders according to their subject by the user. Additionally, the web pages found under top categories of Yahoo site are collected and included in the training examples for diversifying the subject categories to be represented, and the training examples for these categories as well. Our agent employs naive Bayesian learning method that is a well-tested, probability-based categorizing technique. In this paper, the outcome of some experimentation is also outlined and evaluated. A comparison of naive Bayesian learning method alongside other learning methods such as k-Nearest Neighbor and TFIDF is also presented.