• Journal of Periodontal and Implant Science
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• v.23 no.3
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• pp.595-604
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• 1993

Since $Gr{\"{o}}nadhl$ et al introduced a digital subtraction radiography into periodontal dignosis in 1983, many reports using this system has aimed to assess the peroiodontal disease activity and the alveolar bone changes after periodontal treatment. The present study was performed to evaluate the usefulness of digital subtraction radiography for asessing an alveolar bone changes in 3 months after periodontal flap surgery. Serial intraral raiographs were taken from 5 normal subjects and 6 periodontally diseased patients using customized bite blocks attached to film holder in fxation device and digitized by image processing system(consisting of IBM 386, digital frame grabber, CCD camera, Image-pro II software). And the reference parameters were measured by pixel unit and compared with respective radiographs. The serial radiographs showed a little and statistically insignificant difference in reference paramenters. The conventional intraoral radiographs, the subtraction images and the clor enhanced subtraction images were reviewed by 4 examiners and the examiner's agreement rates were compared. The subtraction images and its color enhanced images showed higher examiner's agreement rate than the conventional radiographs. And the propotions of sites diagnosed with bone loss or gain after periodontal surgery were highter in the subtraction images and its color enhanced images than in the conventional radiographs. Especially, in color enhanced images, the unber of bone agin sites tended to increase according to post-surgery periods. These results indicate that projection geometry could be standardized with the divice used in this study, and the subtraction radiography may be useful to assess an alveolar bone changes after periodntal flap surgery.

• Smart Media Journal
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• v.4 no.3
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• pp.50-55
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• 2015

In this paper, we present background modeling method based on Gaussian mixture model to subtract background for night-time video surveillance. In night-time video, it is hard work to distinguish the object from the background because a background pixel is similar to a object pixel. To solve this problem, we change the pixel of input frame to more advantageous value to make the Gaussian mixture model using scaled histogram stretching in preprocessing step. Using scaled pixel value of input frame, we then exploit GMM to find the ideal background pixelwisely. In case that the pixel of next frame is not included in any Gaussian, the matching test in old GMM method ignores the information of stored background by eliminating the Gaussian distribution with low weight. Therefore we consider the stacked data by applying the difference between the old mean and new pixel intensity to new mean instead of removing the Gaussian with low weight. Some experiments demonstrate that the proposed background modeling method shows the superiority of our algorithm effectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.63-66
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• 2009

Automatic segmentation of foreground from background in video sequences has attracted lots of attention in computer vision. This paper proposes a novel framework for the background subtraction that the foreground is segmented from the background by directly subtracting a background image from each frame. Most previous works focus on the extraction of more reliable seeds with threshold, because the errors are occurred by noise, weak color difference and so on. Our method has good segmentations from the approximate seeds by using the Random Walks with Restart (RWR). Experimental results with live videos demonstrate the relevance and accuracy of our algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.349-354
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• 2007

To generate the template of a detected object and to track the overlapped object and the object covered by other objects correctly are important research problems in visual surveillance. The frame difference is not capable of generating the template of slowly moving object. To get around the drawback of the conventional frame difference, we propose a new algorithm for generating a template using adaptive 3-frame difference.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.357-360
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• 2007

물체를 추적하는데 있어서 추적하고자 하는 물체를 검출하여 템플릿을 만드는 것과 두 물체가 겹쳐지거나 다른 배경에 가려진 물체를 구분하여 추적하는 것은 물체 추적에 있어서 중요한 문제이다. 물체를 검출하여 템플릿을 만드는 방법으로 frame difference를 이용하면 천천히 움직이는 물체를 잘 구분할 수 없는 문제점이 있다. 이를 해결하기 위하여 본 논문에서는 adaptive 3-frame difference를 이용하여 정확한 물체의 템플릿을 생성하는 알고리즘을 제안한다.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.365-369
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• 2012

We propose a vision-based method that estimates train position and movement for railway monitoring in which we use a fuzzy classifier to determine train states. The proposed method employs frame difference and background subtraction for estimating train motion and presence, respectively. These features are used as the linguistic variables of the fuzzy classifier. Experimental results show that the proposed method can correctly estimate train position and movement. Therefore the method can be used for railway monitoring systems which estimate crowd density or protect safety.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5856-5864
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• 2011

In this paper, we propose a moving object detection method in road video sequences acquired from a stationary camera. Our proposed method is based on the background subtraction method using Gaussian pyramids in both the background images and input video frames. It is more effective than pixel based subtraction approaches to reduce false detections which come from the mis-registration between current frames and the background image. And to determine a threshold value automatically in subtracted images, we calculate the threshold value using Otsu's method in each frame and then apply a scalar Kalman filtering to the threshold value. Experimental results show that the proposed method effectively detects moving objects in road video images.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.1-6
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• 2019

Foreground/background segmentation in video sequences is often one of the first tasks in machine vision applications, making it a critical part of the system. In this paper, we present an improved sample-based technique that provides robust background image as well as segmentation mask. The conventional multiple interval sampling (MIS) algorithm have suffer from the unbalance of computation time per frame and the rapid change of confidence factor of background pixel. To balance the computation amount, a random-based pixel update scheme is proposed and a spatial and temporal smoothing technique is adopted to increase reliability of the confidence factor. The proposed method allows the sampling queue to have more dispersed data in time and space, and provides more continuous and reliable confidence factor. Experimental results revealed that our method works well to estimate stable background image and the foreground mask.

• Structural Engineering and Mechanics
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• v.31 no.4
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• pp.423-437
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• 2009

In order to perform a step-by-step force-displacement response analysis or dynamic time-history analysis of large buildings with masonry infilled R/C frames, a continuous force-deformation model based on an equivalent strut approach is proposed for masonry infill panels containing openings. The model, which is applicable for degrading elements, can be implemented to replicate a wide range of monotonic force-displacement behaviour, resulting from different design and geometry, by varying the control parameters of the model. The control parameters of the proposed continuous model are determined using experimental data. The experimental program includes fifteen 1/3-scale, single-story, single-bay reinforced concrete frame specimens subjected to lateral cyclic loading. The parameters investigated include the shape, the size, the location of the opening and the infill compressive strength. The actual properties of the infill and henceforth the characteristics needed for the diagonal strut model are based on the assessment of its lateral resistance by the subtraction of the response of the bare frame from the response of the infilled frame.