• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.331-339
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• 2013

LiDAR has been one of the widely used and important technologies for 3D modeling of ground surface and objects because of its ability to provide dense and accurate range measurement. The objective of this research is to develop a method for automatic detection and modeling of railroad power lines using high density LiDAR data and RANSAC algorithms. For detecting railroad power lines, multi-echoes properties of laser data and shape knowledge of railroad power lines were employed. Cuboid analysis for detecting seed line segments, tracking lines, connecting and labeling are the main processes. For modeling railroad power lines, iterative RANSAC and least square adjustment were carried out to estimate the lines parameters. The validation of the result is very challenging due to the difficulties in determining the actual references on the ground surface. Standard deviations of 8cm and 5cm for x-y and z coordinates, respectively are satisfactory outcomes. In case of completeness, the result of visual inspection shows that all the lines are detected and modeled well as compare with the original point clouds. The overall processes are fully automated and the methods manage any state of railroad wires efficiently.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.4 s.19
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• pp.9-15
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• 2005

This research developed the RMIS(Risk Management Information System) which focus on works of risk management fields required of apply of a space information, and focus on the DB to establish and apply the space information efficiently with research scope on the LPG refueling station in city. On the basis of the RMIS, this research provides the baseline to lead on an efficiency of safety inspection of LPG refueling station, advance risk assessment, and efficient making decision of an accident correspondence assessment with interlocking the GIS representing risk through the automation of a quantitative risk assessment standardize requirement to control at real-time. The RMIS development process is as follows. firstly, Relational Database(RDB) was developed by using fundamental data both On-site and Off-site relating data as peforming risk assessment on the LPG refueling station in city. Second, the risk management integral database system was developed to monitor and control the risk efficiently for user with using the Visual Basic Program. Third, through interlocking the risk management integral database system and the GIS(Falcon-map) was suggested the decision making method. Represented results through out the RMIS program development are as follows. Firstly, the RMIS was established the mutual information to advance management the risk efficiently for user and inspector with using the risk management data. Second, as this study managed risk for on-site and off-site separately and considered effect for inside and outside of facility, constructed the basis on safety management which can respond to major accident. Third, it was composed the baseline to making decision that on the basis of user interface.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.178-187
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• 2018

This study aimed to develop a model of the program for automation measuring the preference of the portraits based on the relationship between the image quality factors and the preferences in the portraits for manufacturers aiming at high utilization of the users. in order to proceed with the evaluation, the image quality measurement was divided into objective and subjective items, and the evaluation was done through image processing and statistical methods. the image quality measurement items can be divided into objective evaluation items and subjective evaluation items. RSC Contrast, Dynamic Range and Noise were selected for the objective evaluation items, and the numerical values were statistically analyzed and evaluated through the program. Exposure, Color Tone, composition of person, position of person, and out of focus were selected for subjective evaluation items and evaluated by image processing method. By applying objective and subjective assessment items, the results were very accurate, with the results obtained by the developed program and the results of the actual visual inspection. but since the currently developed program can be evalua ted only after facial recognition of the person, future research will need to develop a program that can evaluate all kinds of portraits.

• Journal of the Korean GEO-environmental Society
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• v.21 no.10
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• pp.11-16
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• 2020

Korea's agricultural reservoir is one of the country's major infrastructures and plays an important role in people's lives. However, aging reservoirs are a risk for life and property. Currently, large and small dams and reservoirs have been constructed nationwide for more than 40 years of aging. Dams and reservoirs built nationwide are managed by various institutions. Therefore, it is difficult to manage all dams and reservoirs due to cost and time. Managers in the field with less management personnel and lack of expertise should be able to quickly identify risk factors for multiple reservoirs. In this study, risk factors such as seepage, leakage, settlement slide, crack and erosion were selected. To assess the risk of the items, we used the analytical hierarchical process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods. The analysis showed that seepage has the greatest impact on reservoir collapse. It is judged that the priority of detailed diagnosis can be determined by evaluating the risk of dam reservoir collapse in a convenient way in advance using the calculated weight.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.