• Journal of Navigation and Port Research
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• v.28 no.10 s.96
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• pp.917-923
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• 2004

With a rapid growing of information networks and development achievements in mobile technology the spatial information is one of the most important resources for modern daily life. But the speed of spatial information technology trends are far from the real service demands as compared to the other relevant fields, and it would be natural that this unbalanced gap between the supply and demand of spatial information technology is being resulted from the absence of appropriate modeling concepts at some extents. In this paper there would be shown a new approaching model for the spatial information system based on agent concepts, which is able to perform some spatial tasks if properly implemented afterward. And to give resonable background of the new modeling framework here also some known critics for the commonly used modeling approaches when they applied to spatial information modeling followed by several alternative requirements for a good spatial modeling framework. And also there would be some considerations for applying this approach to the marine geographic information communities.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.405-412
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• 2010

Previous background generation techniques showed bad performance in complex environments since they used only temporal contexts. To overcome this problem, in this paper, we propose a new background generation method which incorporates spatial as well as temporal contexts of the image. This enabled us to obtain 'clean' background image with no moving objects. In our proposed method, first we divided the sampled frame into m*n blocks in the video sequence and classified each block as either static or non-static. For blocks which are classified as non-static, we used MRF framework to model them in temporal and spatial contexts. MRF framework provides a convenient and consistent way of modeling context-dependent entities such as image pixels and correlated features. Experimental results show that our proposed method is more efficient than the traditional one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.396-402
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• 2021

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.99-106
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• 2008

A synthetic aperture radar (SAR) system is of great interest in many fields of civil and military applications because of all-weather and luminance free imaging capability. SAR image quality parameters such as spatial resolution, peak to sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) can be normally estimated by modeling of impulse response function (IRF) which is obtained from various system design parameters such as altitude, operational frequency, PRF, etc. In modeling of IRF, however, background clutter environment surrounding the IRF is generally neglected. In this paper, analysis method for SAR mage quality is proposed in the real background clutter environment. First of all, SAR raw data of a point scatterer is generated based on various system parameters. Secondly, the generated raw data can be focused to ideal IRF by range Doppler algorithm (RDA). Finally, background clutter obtained from image of currently operating SAR system is applied to IRF. In addition, image quality is precisely analyzed by zooming and interpolation method for effective extraction of IRF, and then the effect of proposed methodology is presented with several simulation results under the assumption of estimation error of Doppler rate.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.147-159
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• 2023

This study is a study to develop a model for measurement and processing of experience data, which is emerging as a core value in quality management of medical services. In the theoretical background, a literature study was conducted on the importance of experience in medical service, measurement and processing of experience data, and realization of patient-centeredness. Based on these literature and theoretical background research results, operational definitions were performed for the following four research variables, and statistical tests were conducted. Hypothesis 1 is the effect of measuring experience data from the perspective of three factors on persona modeling, Hypothesis 2 is the effect of persona modeling on service blueprint visualization, Hypothesis 3 is the effect of service blueprint visualization on realization of patient-centeredness, and Hypothesis 4 is persona modeling This is the effect that modeling has on the realization of patient-centeredness. After data-based testing of factor analysis, reliability analysis, and correlation analysis, all four hypotheses were adopted as a result of verification using regression analysis. In conclusion, in an era where it is difficult to recognize the value of having only good medical staff and medical equipment in hospitals, it was possible to grasp the meaning that what kind of medical service experience is continuously obtained is more important to patients than the effectiveness of medical staff and medical equipment. In the era of the service economy, the core of hospital service competitiveness is providing attractive experiences, which is the real strength of hospitals, so the measurement and processing of experience data, which is the subject of this study, will have an important meaning in realizing patient-centeredness and realizing smart hospitals.

• Journal of Environmental Health Sciences
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• v.37 no.1
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• pp.73-79
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• 2011

The growing interest in nanotechnology has resulted in increasing concern and a number of published environmental and workplace measurements for assessing occupational exposure to engineered nanomaterials. However, the amount of previous exposure data remains limited. Furthermore the data available was collected with extensive variation in terms of exposure measurement strategy, which limits the ability to pool the data in the future. In response, this paper reviewed several pertinent issues related to exposure measurement strategy to suggest a harmonized measurement strategy which would make exposure data more useful in the future, e.g. correlation between exposure metrics, relationship between activity and exposure, task-based or shift-based assessment, background concentration, limitation of personal exposure monitoring and other determinants of exposure/modeling. An improved sampling strategy for nanomaterial exposure assessment should be considered in order to maximize the use of the data from various real time monitoring instruments.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1211-1212
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• 2008

In this paper, we propose image process modeling as a part of location based surveillance system for unauthorized target recognition and tracking in harbor, airport, military zone. For this, we compress and store background image in lower resolution and perform object extraction and motion tracking by using sobel edge detection and difference picture method between real images and a background image. In addition to, we use Independent Component Analysis Neural Network for moving target recognition. Experiments are performed for object extraction and tracking of moving targets on road by using static camera in 20m height building and it shows the robust results.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.421-424
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• 1999

Automatic human face detection in a complex background is one of the difficult problems In this paper. we propose an effective automatic face detection system that can locate the face region in natural scene images when the system is used as a pre-processor of a face recog- nition system. We use two natural and powerful visual cues, the color and the human head shape. The outline of the human head can be generally described as being roughly elliptic in nature. In the first step of the proposed system, we have tried the approach of fitting the best Possible ellipse to the outline of the head In the next step, the method based on the human skin color space by selecting flesh tone regions in color images and histogramming their r(＝R/(R＋G＋B)) and g(＝G/R＋G＋B)) values. According to our experiment. the proposed system shows robust location results

• Journal of Ubiquitous Convergence Technology
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• v.1 no.1
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• pp.3-8
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• 2007

This paper presents a set of techniques used in a real-time visual surveillance system. The system is implemented on a low-cost embedded DSP platform that is designed to work with stationary video sources. It consists of detection, a tracking and a classification module. The detector uses a statistical method to establish the background model and extract the foreground pixels. These pixels are grouped into blobs which are classified into single person, people in a group and other objects by the dynamic periodicity analysis. The tracking module uses mean shift algorithm to locate the target position. The system aims to control the human density in the surveilled scene and detect what happens abnormally. The major advantage of this system is the real-time capability and it only requires a video stream without other additional sensors. We evaluate the system in the real application, for example monitoring the subway entrance and the building hall, and the results prove the system's superior performance.