• Journal of the Semiconductor & Display Technology
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• v.8 no.2
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• pp.23-29
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• 2009

To increase the data capacity of one-dimensional symbology, 2D barcodes have been proposed a decade ago. In this paper, a new 2D barcode detection algorithm based on Local Binary Pattern is presented. To locate 2D barcode symbols, a texture analysis scheme based on the Local Binary Pattern is adopted, and a gray-scale projection with sub-pixel operation is utilized to separate the symbol precisely from the input image. Finally, the segmented symbol is normalized using the inverse perspective transformation for the decoding process. The proposed method ensures high performances under various lighting/printing conditions and strong perspective deformations. Experiments show that our method is very robust and efficient in detecting the symbol area for the various types of 2D barcodes.

• Journal of Radiation Protection and Research
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• v.47 no.4
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• pp.214-225
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• 2022

Background: The conventional cerium-doped Gd₂Al₂Ga₃O₁₂ (GAGG(Ce)) scintillator-based gamma-ray imager has a bulky detector, which can lead to incorrect positioning of the gammaray source if the shielding against background radiation is not appropriately designed. In addition, portability is important in complex environments such as inside nuclear power plants, yet existing gamma-ray imager based on a tungsten mask tends to be weighty and therefore difficult to handle. Motivated by the need to develop a system that is not sensitive to background radiation and is portable, we changed the material of the scintillator and the coded aperture. Materials and Methods: The existing GAGG(Ce) was replaced with Bi₄Ge₃O₁₂ (BGO), a scintillator with high gamma-ray detection efficiency but low energy resolution, and replaced the tungsten (W) used in the existing coded aperture with lead (Pb). Each BGO scintillator is pixelated with 144 elements (12 × 12), and each pixel has an area of 4 mm × 4 mm and the scintillator thickness ranges from 5 to 20 mm (5, 10, and 20 mm). A coded aperture consisting of Pb with a thickness of 20 mm was applied to the BGO scintillators of all thicknesses. Results and Discussion: Spectroscopic characterization, imaging performance, and image quality evaluation revealed the 10 mm-thick BGO scintillators enabled the portable gamma-ray imager to deliver optimal performance. Although its performance is slightly inferior to that of existing GAGG(Ce)-based gamma-ray imager, the results confirmed that the manufacturing cost and the system's overall weight can be reduced. Conclusion: Despite the spectral characteristics, imaging system performance, and image quality is slightly lower than that of GAGG(Ce), the results show that BGO scintillators are preferable for gamma-ray imaging systems in terms of cost and ease of deployment, and the proposed design is well worth applying to systems intended for use in areas that do not require high precision.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3006-3016
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• 2022

In this study, the images of specific prompt gamma (PG)-rays of 719 keV emitted from proton-boron reactions were analyzed using single-photon emission computed tomography (SPECT). Quantitative evaluation of the images verified the detection of anatomical changes in tumors, one of the important factors in daily adaptive proton therapy (DAPT) and verified the possibility of application of the PG-ray images to DAPT. Six scenarios were considered based on various sizes and locations compared to the reference virtual tumor to observe the anatomical alterations in the virtual tumor. Subsequently, PG-rays SPECT images were acquired using the modified ordered subset expectation-maximization algorithm, and these were evaluated using quantitative analysis methods. The results confirmed that the pixel range and location of the highest value of the normalized pixel in the PG-rays SPECT image profile changed according to the size and location of the virtual tumor. Moreover, the alterations in the virtual tumor size and location in the PG-rays SPECT images were similar to the true size and location alterations set in the phantom. Based on the above results, the tumor anatomical alterations in DAPT could be adequately detected and verified through SPECT imaging using the 719 keV PG-rays acquired during treatment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.625-635
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• 2014

Multiple strips are required for large area mapping using ALS (Airborne Laser Scanner) system. LiDAR (Light Detection And Ranging) data collected from the ALS system has discrepancies between strips due to systematic errors of on-board laser scanner and GPS/INS, inaccurate processing of the system calibration as well as boresight misalignments. Such discrepancies deteriorate the overall geometric quality of the end products such as DEM (Digital Elevation Model), building models, and digital maps. Therefore, strip adjustment for minimizing discrepancies between overlapping strips is one of the most essential tasks to create seamless point cloud data. This study implemented area-based matching (ABM) to determine conjugate features for computing 3D transformation parameters. ABM is a well-known method and easily implemented for this purpose. It is obvious that the exact same LiDAR points do not exist in the overlapping strips. Therefore, the term "conjugate point" means that the location of occurring maximum similarity within the overlapping strips. Coordinates of the conjugate locations were determined with sub-pixel accuracy. The major drawbacks of the ABM are sensitive to scale change and rotation. However, there is almost no scale change and the rotation angles are quite small between adjacent strips to apply AMB. Experimental results from this study using both simulated and real datasets demonstrate validity of the proposed scheme.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.919-927
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• 2019

In this paper, we propose an accurate camera calibration method for acquiring multiview stereoscopic images. Generally, camera calibration is performed by using checkerboard structured patterns. The checkerboard pattern simplifies feature point extraction process and utilizes previously recognized lattice structure, which results in the accurate estimation of relations between the point on 2-dimensional image and the point on 3-dimensional space. Since estimation accuracy of camera parameters is dependent on feature matching, accurate detection of checkerboard corner is crucial. Therefore, in this paper, we propose the method that performs accurate camera calibration method through accurate detection of checkerboard corners. Proposed method detects checkerboard corner candidates by utilizing 1-dimensional gaussian filters with succeeding corner refinement process to remove outliers from corner candidates and accurately detect checkerboard corners in sub-pixel unit. In order to verify the proposed method, we check reprojection errors and camera location estimation results to confirm camera intrinsic parameters and extrinsic parameters estimation accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.911-913
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• 2016

Recently barcode detection using a camera is popular, but the recognition performance is low at the effectively low-resolution. The paper propose sub-pixel synchronization technique for better recognition performance. The experiments with ITF-18 demonstrates its performance gain (66% for CIF, 100% for VGA) against the existing recognition algorithms.

• Transactions of Materials Processing
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• v.19 no.5
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• pp.320-324
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• 2010

Multi-Layer Ceramic Circuit(MLCC) in the face of thousands of fine pitch multi hole is processed. However, the fine pitch multi hole has a size of only a few micrometers. Therefore, in order to curtail the measurement time and reduce error, the image processing measurement method is required. So, we proposed an image processing measurement algorithm which is required to accurately measure the fine pitch multi hole. The proposed algorithm gets image of the fine pitch multi hole, extracts object from the image by morphological process, and extracts the parameters of its position and feature by edge detecting process. In addition, we have used the sub-pixel algorithm to improve accuracy. As a result, the proposed algorithm shows 97% test-retest measurement reliability within 2 ${\mu}m$. We found that the algorithm was wellsuited for measuring the fine pitch multi hole.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.485-488
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• 2013

최근 자동차 산업의 활성화로 인해 교통사고 급증이 사회 문제화 되면서 사고를 미연에 방지할 수 있는 운전자 보조 시스템 연구가 활발하게 이루어지고 있다. 일반적으로 자동차 사고 원인의 70% 이상이 운전자 과실에 의해서 발생되고 전체 추돌사고의 75%가 시속 29km 이하의 속도에서 발생한다. 이를 예방하기 위해서 운전자의 인지 판단을 보조하는 시스템의 개발이 많이 이루어지고 있는데, 예를 들어 자동 주차 시스템, AVM(Around View Monitoring) 시스템 등이 있다. 본 논문에서는 AVM 시스템 중 원근 왜곡을 보정하는 단계에서 직선 및 교점을 검출할 때, NMS(Non-Maximum Suppression)를 적용한 허프 변환 방법을 사용할 것이다. 또한 기존의 Sub-Pixel을 이용한 직선 및 교점 검출 방법과 NMS을 적용한 허프 변환 방법을 사용한 직선 및 교점을 검출하는 방법을 비교 분석함으로써 제안하는 NMS를 적용한 허프변환을 이용한 직선 및 교점을 검출하는 방법을 사용하여 보다 효율적인 AVM 시스템의 구현 가능성을 검증한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.499-503
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• 2013

자동차를 위한 전방향(omni-directional) 감시 시스템, 로봇의 시각 역할 등 다양한 비전 시스템에서 카메라가 장착되어 사용되고 있다. AVM(Around View Monitoring) 시스템에서 그리드 패턴의 코너를 검출하기 위해서는 먼저, 광각 카메라에서 획득한 비선형적인 방사 왜곡을 가진 영상의 왜곡 보정 작업을 수행하여야 한다. 이후에 왜곡이 보정된 영상 내부의 그리드 패턴 각 코너들을 자동으로 검출하기 위해서 Sub-Pixel, 허프 변환 등의 여러 가지 방법이 있으며 현재 출시된 AVM 시스템에 직선이나 교점 및 코너 검출을 위해 사용되고 있다. 본 논문에서는 왜곡 보정 영상을 입력 영상으로 받아 그리드 패턴의 코너를 자동으로 검출하는 프로그램을 설계한다. 제안하는 코너 검출 방법을 직접 구현하여 성능을 평가함으로써 AVM 시스템에서 코너를 검출하는 부분에 적용시킬 수 있음을 보인다.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.