• Journal of the Korean Geotechnical Society
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• v.18 no.6
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• pp.153-160
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• 2002

A new system identification method based on tunnel deformation data is proposed to find the damage in the lining structure. For this, an inverse problem in which the deformation data and dead load of concrete lining are known a priori is introduced to estimate the degree and location of the damages. Models based on uniform reduction of stiffness and homogenized crack concept are individually employed to compare the applicability and relative advantages of the models. Numerical analyses are peformed for the idealized tunnel structure and the effect of white noise, common in most measurement data, is also included to better understand the suitability of the proposed models. As a result, model 1 based on uniform stiffness reduction method is shown to be relatively insensitive to the noise, while model 2 with the homogenized crack concept is proven to be easily applied to the field situation since the effect of stiffness reduction is rather small.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.1-9
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• 2008

Thanks to the multimedia technology development, it is possible to show image representations in high quality and to process images in various ways. Anisotropic diffusion as an effective diffusion filtering among many image preprocessing methods and postprocessing methods is used in reduction of speckle noises of ultrasound images, image restoration, edge detection, and image segmentation. However, the conventional anisotropic diffusion based on a cross-kernel causes the following problems. The problem is the concentration of edges in the vertical or horizontal directions. In this paper, a new anisotropic diffusion transform based on directions of gradient is proposed. The proposed method uses the eight directional square-kernel which is an expanded form of the cross-kernel. The proposed method is to select directions of small gradient based on square-kernel. Therefore, the range of proposed diffusion is selected adaptively according to the number of the directions of gradient. Experimental results show that the proposed method can decrease the concentration of edges in the vertical or horizontal directions, remove impulse noise. The image in high quality can be obtained as a result of the proposed method.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.151-156
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• 2005

In this paper, we propose feature based character watermarking methods based on geometical features specific to characters of text in document image. The proposed methods can satisfy both data capacity and robustness simultaneously while none of the conventional methods can. According to the characteristics of characters, watermark can be embed or detected through changes of connectivity of the characters, differences of characteristics of edge pixels or changes of area of holes. Experimental results show that our identification techniques are very robust to distortion and have high data capacity.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.41-51
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• 2010

In the research for driver assistance systems, traffic sign information to the driver must be a very important information. Therefore, the detection system of traffic signs located on the road should be able to handel real-time. To detect the traffic signs, color and shape of traffic signs is to use the information after images obtained using the CCD camera. In the road environment, however, using color information to detect traffic sings will cause many problems due to changes of weather and environmental factors. In this paper, to solve it, the candidate traffic sign regions are detected from road images obtained in a variety of the illumination changes using the HSI eign-color model. And then, using the invariant moment-based SVM classifier to detect traffic signs are proposed. Experimental results show that, traffic sign detection rate is 91%, and the processing time per frame is 0.38sec. Proposed method is useful for real-time intelligent traffic guidance systems can be applied.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.307-313
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• 2021

In this paper, a lightweight network with fewer parameters compared to the existing object detection method is proposed. In the case of the currently used detection model, the network complexity has been greatly increased to improve accuracy. Therefore, the proposed network uses EfficientNet as a feature extraction network, and the subsequent layers are formed in a pyramid structure to utilize low-level detailed features and high-level semantic features. An attention process was applied between pyramid structures to suppress unnecessary noise for prediction. All computational processes of the network are replaced by depth-wise and point-wise convolutions to minimize the amount of computation. The proposed network was trained and evaluated using the PASCAL VOC dataset. The features fused through the experiment showed robust properties for various objects through a refinement process. Compared with the CNN-based detection model, detection accuracy is improved with a small amount of computation. It is considered necessary to adjust the anchor ratio according to the size of the object as a future study.

• Journal of radiological science and technology
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• v.40 no.4
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• pp.567-573
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• 2017

The purpose of this study was to evaluate whether or not there was artifact when the upper limb could not be lifted to the top of the head during multi-detector computed tomography(MDCT) scans of the chest and abdomen. Contrast radiography of the human and chest phantom was performed with 128channal MDCT. Under the same conditions(120 kVp, 110 mAs, standard algorithm)both hands lifted up and put down each time in the human experiment. In the chest phantom experiment, the radiography was carried out when the upper limb phantom was adjusted at a certain distance(0, 3, 7 cm) from the chest phantom. Subsequently, the values of Noise, CT number, SNR, and CNR were measured in the field of concern. The noise value of fat, rib, and muscle increased when the arm was lifted in humans(0.79, 47.8, 27%). Furthermore, when the upper limb was lowered, the noise value of muscle and lung increased in the phantom(31.2, 9.4%). In addition, the noise value of the muscles and lung decreased by 5, 25.12% and 5.6, 15.35% as the upper limb moved about 0,3,7cm away from the chest. When the chest and abdominal radiography were performed, in the case of the presence of other parts outside the inspection area, the probability of artifact was minimal while the distance was more than 3cm away from the upper limb to the chest and abdomen.

• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.89-95
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• 2016

The purpose of this experiment intend to evaluate the quality of the image based on the orbit and basal ganglia with high radiosensitivity for the noise, SNR and dose using the five kinds patient fixing pad in brain phantom MDCT(BrillianceTM CT 64 slice, PHILIPS, Netherward). The noise had a higher values in AP than those of others, but the SNR was lower in AP than those of others. The SNR was higher in UP than those of RP, PP, SP and AP. The UP, RP and PP were no statistically significant(p>0.05), whereas it was significant difference between UP, RP, PP and SP, AP(p<0.05). This is causes of the noise difference is generated due to the differences in the radiation absorption dose in accordance with each the component of the absorbed dose level of the detector according to the reference line and each of SOML when the radiation exposured. The CTDIvol(mGy) and DLP of orbit and basal ganglia were 56.95, 911.50, respectively. There is no difference between both mean dose. In conclusion, it is possible to distinguish among a kind of 5 patient fixing pad by using brain phantom MDCT. Overall, patient fixing pad of UP, RP and PP based on a brain phantom MDCT can provide useful information.

• Journal of radiological science and technology
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• v.39 no.2
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• pp.227-236
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• 2016

Dynamic positron emission tomography(dPET) is widely used medical imaging modality that can provide both physiological and functional neuro-image for diagnosing various brain disease. However, dPET images have low spatial-resolution and high noise level during spatio-temporal analysis (three-dimensional spatial information + one-dimensional time information), there by limiting clinical utilization. In order to overcome these issues for the spatio-temporal analysis, a novel computational technique was introduced in this paper. The computational technique based on singular value decomposition classifies multiple independent components. Signal components can be distinguished from the classified independent components. The results show that signal to noise ratio was improved up to 30% compared with the original images. We believe that the proposed computational technique in dPET can be useful tool for various clinical / research applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.5
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• pp.909-916
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• 2009

This paper presents the automatic vision algorithm to generate and calibrate reference phase plane to improve the accuracy of 3D measuring machine of using phase shifting projection moire method, which is not traditional N-bucket method, but is based on direct image processing method to the pattern projection image. Generally, to acquire accurate reference phase plane, the calibration specimen with well treated surface is needed, and detailed calibration method should be performed. For the cost reduction of specimen manufacturing and the calibration time reduction, on the specimen, not specially designed, with general accuracy level, an efficient calibration procedure for the reference phase generation is proposed. The proposed vision algorithm is developed to extract the line center points of the projected line pattern from acquired images, derive the line feature information consisting of its slope and intercept by using sampled feature points, and finally generate the related reference phase between line pairs. Experimental results show that the proposed method make reference phase plane with a good accuracy under noisy environment and the proposed algorithm can reduce the total cost to make high accurate calibration specimen, also increase the accuracy of reference phase plane, and reduce the complex calibration procedure to move grid via N-bucket algorithm precisely.

• Journal of KIISE
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• v.42 no.5
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• pp.642-651
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• 2015

A vanishing point is a point where parallel lines converge, and they become evident when a camera's lenses are used to project 3D space onto a 2D image plane. Vanishing point detection is the use of the information contained within an image to detect the vanishing point, and can be utilized to infer the relative distance between certain points in the image or for understanding the geometry of a 3D scene. Since parallel lines generally exist for the artificial structures within images, line-detection-based vanishing point-detection techniques aim to find the point where the parallel lines of artificial structures converge. To detect parallel lines in an image, we detect edge pixels through edge detection and then find the lines by using the Hough transform. However, the various textures and noise in an image can hamper the line-detection process so that not all of the lines converging toward the vanishing point are obvious. To overcome this difficulty, it is necessary to assign a different weight to each line according to the degree of possibility that the line passes through the vanishing point. While previous research studies assigned equal weight or adopted a simple weighting calculation, in this paper, we are proposing a new method of assigning weights to lines after noticing that the lines that pass through vanishing points typically belong to artificial structures. Experimental results show that our proposed method reduces the vanishing point-estimation error rate by 65% when compared to existing methods.