• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.527-532
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• 2013

Depth estimation is an essential factor for robot vision, 3D scene modeling, and motion control. The depth estimation method is based on focusing values calculated in a series of images by a single camera at different distance between lens and object. In this paper, we proposed a relative depth estimation method using focus measure. The proposed method is implemented by focus value calculated for each image obtained at different lens position and then depth is finally estimated by considering relative distance of two patterns. We performed various experiments on the effective focus measures for depth estimation by using various patterns and their usefulness.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.373-380
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• 2014

Malicious codes uses generic unpacking technique to make it hard for analyzers to detect their programs. Recently their has been several researches about generic packet to prevent or detect these techniques. And they try to focus on the codes that repeats while generic packing is doing compression because generic packing technique executes after it is decompressed. And they try to focus on the codes that repeats while generic packing is doing compression because generic packing technique executes after it is decompressed. Therefore, this makes a interesting performance which shows a similar address value from the codes which are repeated several times what is different from the normal program codes. By dividing these codes into regularly separated areas we can find that the generic unpacking codes have a small entropy value compared to normal codes. Using this method, it is possible to identify any program if it is a generic unpacking code or not even though we do not know what kind of algorithm it uses. This paper suggests a way of disarming the generic codes by using the low value entropy value which comes out from the Opcode addresses when generic unpacking codes try to decompress.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.3
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• pp.74-82
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• 2004

This paper proposes a fast zooming and focusing technique for implementing a real-time surveillance camera system which can capture a face image in less than 1 second. It determines the positions of zooming and focusing lenses using two-step algorithm. In the first step, it moves the zooming and focusing lenses simultaneously to the positions calculated using the lens equations for achieving the predetermined magnification. In the second step the focusing lens is adjusted so that it is positioned at the place where the focus measure is the maximum. The camera system implemented for the experiments has shown that the proposed algorithm spends about 0.56 second on average fur obtaining a focused image.

• Journal of the Korean Society of Radiology
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• v.10 no.4
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• pp.247-253
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• 2016

Ultrasound devices diagnose many disease, which is widely used, can not be standardized quantitative evaluated in order to evaluate sonography image of quality. Therefore, in this papers, aims to get correct image in order to accurate diagnosis by figuring out the appropriate parameter based on each target by measuring distortion which results in the analyzation of the sensitivity of SNR and the histogram of signal by manipulating parameter of 8 mm target in ATS-539 multipurpose phantom. Equipment using Acuson sequoia 512, convex probe and utilizes multi-objective phantom. experiment method is that first you put the phantom on the flat and acquire 85 sheets of image, changing frequency(2,3,4 MHz, harmonic 3, 4, 4.5 MHz), Focus(2, 4, 6 unit), and Dynamic Range(58, 68, 78, 88, 98) for a 8 mm structure. through the Image J program. The sensitivity angle of 8mm target through Image J program is gauged by each separate target SNR and the distorted angle subtract and measure Histogram of background from Histogram of signal and take top 40% from the given result value above. According to parameter variation we found out proper parameter by acquiring SNR of sensitivity and distortion data for aspect of transition. The more this findings have Focus, the lower distortion value and at 4 MHz frequency this result have high SNR and low distortion value. Dynamic Range got an appropriate image on 88 and 98. It is considered on the basis of the experimental data, the probability of disease diagnosis will get higher.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.149-160
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• 2014

Recently studies on application development and utilization using sensors and devices embedded in smartphones have flourished at home and abroad. This study aimed to analyze the accuracy of the images of smartphone to determine three-dimension position of close objects prior to the development of photogrammetric system applying smartphone and evaluate the feasibility to use. First of all, camera calibration was conducted on autofocus and infinite focus. Regarding camera calibration distortion model with balance system and unbalance system was used for the decision of lens distortion coefficient, the results of calibration on 16 types of projects showed that all cases were in RMS error by less than 1 mm from bundle adjustment. Also in terms of autofocus and infinite focus on S and S2 model, the pattern of distorted curve was almost the same, so it could be judged that change in distortion pattern according to focus mode is very little. The result comparison according to autofocus and infinite focus and the result comparison according to a software used for multi-image processing showed that all cases were in standard deviation less than ${\pm}3$ mm. It is judged that there is little result difference between focus mode and determination of three-dimension position by distortion model. Lastly the checkpoint performance by total station was fixed as most probable value and the checkpoint performance determined by each project was fixed as observed value to calculate statistics on residual of individual methods. The result showed that all projects had relatively large errors in the direction of Y, the direction of object distance compared to the direction of X and Z. Like above, in terms of accuracy for determination of three-dimension position for a close object, the feasibility to use smartphone camera would be enough.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.243-248
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• 2013

In this paper, depth of foreground is analysed by focus and color analysis grouping for 2D/3D video conversion and depth of foreground progressing method is preposed by using focus and motion information. Candidate foreground image is generated by estimated movement of image focus information for extracting foreground from 2D video. Area of foreground is extracted by filling progress using color analysis on hole area of inner object existing candidate foreground image. Depth information is generated by analysing value of focus existing on actual frame for allocating depth at generated foreground area. Depth information is allocated by weighting motion information. Results of previous proposed algorithm is compared with proposed method from this paper for evaluating the quality of generated depth information.

• Journal of the Korean Vacuum Society
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• v.20 no.6
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• pp.403-408
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• 2011

To develop the MFX (Micro-Focus X-ray) tube, the trajectories of electrons emitted from the field emission cathode was simulated using SIMION program. Regardless of starting position of the electron in emitter, we found out the fact that there is the optimum extractor voltage Ve, which can focus the electron beam on one place. Extractor voltage Ve varies depending on the source voltage Vs, but the ratio of two voltages (Ve/Vs) is always constant, its value was 99.4%. When the ratio of two voltages (Ve/Vs) was 99.4%, the beam diameter in the cross-over point was $1.2{\mu}m$. Because the focal spot size in MFXG (Micro-Focus X-ray Generator) can not be less than the cross-over diameter within MFX tube, it is important to find out the conditions that can make a smaller beam diameter. Therefore, the above results is considered to be a very important ones in the development of the MFXG.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.531-534
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• 2012

본 논문에서는 Active Contour 기반의 영역 분할에서 이미지의 초점값을 이용하여 분할된 영역 사이의 경계를 생성하여 기존의 Active Contour에서 발생할 수 있는 중첩 객체의 동일 객체 인식을 방지하는 기법을 제안한다. Active Contour는 영상에서 객체의 윤곽을 검출하여 윤곽을 기준으로 영상을 분할하지만 중첩되거나 근접한 객체에서의 분할이 정확하게 이루어지지 않아 동일 객체로 인식하는 단점이 있다. 이러한 객체에서의 분할을 위해 영상의 초점값을 이용하여 영상 내에 존재하는 객체의 유사 경계 영역을 생성하고 Active Contour의 결과에 적용하여 경계를 생성한 뒤 초점값 적용으로 인해 생성될 수 있는 홀 영역을 hole filling 과정을 수행하여 보완함으로써 보다 정확한 객체를 추출하였다.

• Journal of IKEEE
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• v.23 no.3
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• pp.817-825
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• 2019

Difference equation is useful for control algorithm in the microprocessor. To approximate a derivative values from sampled data, it is used the methods of forward, backward and central differences. The key of computing discrete derivative values is the finite difference coefficient. The focus of this paper is a new approach method of finite difference formula. And we apply the proposed method to the recursive least squares(RLS) algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.30-39
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• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.