• Title/Summary/Keyword: shape detection

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Image Restoration using Weighted Cross-Shape Median Filter (가중격자형 메디안 필터를 이용한 영상복원)

  • Na, Cheol-Hun;Kim, Su-Yeong;Han, Man-Soo;Kang, Seong-Jun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.10a
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    • pp.711-714
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    • 2015
  • A new technique for image restoration using Weighted cross-shaped median filter with edge-detection algorithm is proposed in this paper. It consists of simple hypothesis test for edge-detection, and makes use of the weighted cross-shape window. This method is applied to noise corrupted image and its results are compared with those of median filters. As for the experimental result, method of weighted cross-shape median filter is superior to other median filters.

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Screw Transformation Mechanism of Screw-Propelled Robot for Efficient Void Detection in Grease Pipe (스크류 추진형 검측 로봇의 효율적인 검측을 위한 스크류 구조 변화 메커니즘)

  • Kim, Dongseon;Kim, Hojoong;Kim, Jinhyun
    • The Journal of Korea Robotics Society
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    • v.17 no.2
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    • pp.172-177
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    • 2022
  • In general, detection robots using ultrasonic sensors are equipped with sensors to protrude outward or to contact objects. However, in the case of a screw-propelled robot that detects the inside of a reactor tendon duct, if the ultrasonic sensor protrudes to the outside, resistance due to grease is generated, and thus the propulsion efficiency is reduced. In order to increase the propulsion efficiency, the screw must be sharp, and the sharper the screw, the more difficult it is to apply a high-performance ultrasonic sensor, and the detection efficiency decreases. This paper proposes a screw shape-changing mechanism that can improve both propulsion efficiency and detection efficiency. This mechanism includes an overlapped helical ring (OHR) structure and a magnetic clutch system (MCS), and thus the shape of a screw may be changed to a compact size. As a result, the Screw-propelled robot with this mechanism can reduce the overall length by about 150 mm and change the shape of the screw faster and more accurately than a robot with a linear actuator.

Improved fast neutron detection using CNN-based pulse shape discrimination

  • Seonkwang Yoon;Chaehun Lee;Hee Seo;Ho-Dong Kim
    • Nuclear Engineering and Technology
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    • v.55 no.11
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    • pp.3925-3934
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    • 2023
  • The importance of fast neutron detection for nuclear safeguards purposes has increased due to its potential advantages such as reasonable cost and higher precision for larger sample masses of nuclear materials. Pulse-shape discrimination (PSD) is inevitably used to discriminate neutron- and gamma-ray- induced signals from organic scintillators of very high gamma sensitivity. The light output (LO) threshold corresponding to several MeV of recoiled proton energy could be necessary to achieve fine PSD performance. However, this leads to neutron count losses and possible distortion of results obtained by neutron multiplicity counting (NMC)-based nuclear material accountancy (NMA). Moreover, conventional PSD techniques are not effective for counting of neutrons in a high-gamma-ray environment, even under a sufficiently high LO threshold. In the present work, PSD performance (figure-of-merit, FOM) according to LO bands was confirmed using a conventional charge comparison method (CCM) and compared with results obtained by convolution neural network (CNN)-based PSD algorithms. Also, it was attempted, for the first time ever, to reject fake neutron signals from distorted PSD regions where neutron-induced signals are normally detected. The overall results indicated that higher neutron detection efficiency with better accuracy could be achieved via CNN-based PSD algorithms.

Development of a ladder-shape melting temperature isothermal amplification (LMTIA) assay for detection of African swine fever virus (ASFV)

  • Wang, Yongzhen;Wang, Borui;Xu, Dandan;Zhang, Meng;Zhang, Xiaohua;Wang, Deguo
    • Journal of Veterinary Science
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    • v.23 no.4
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    • pp.51.1-51.10
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    • 2022
  • Background: Due to the unavailability of an effective vaccine or antiviral drug against the African swine fever virus (ASFV), rapid diagnosis methods are needed to prevent highly contagious African swine fever. Objectives: The objective of this study was to establish the ladder-shape melting temperature isothermal amplification (LMTIA) assay for the detection of ASFV. Methods: LMTIA primers were designed with the p72 gene of ASFV as the target, and plasmid pUC57 was used to clone the gene. The LMTIA reaction system was optimized with the plasmid as the positive control, and the performance of the LMTIA assay was compared with that of the commercial real-time polymerase chain reaction (PCR) kit in terms of sensitivity and detection rate using 200 serum samples. Results: Our results showed that the LMTIA assay could detect the 104 dilution of DNA extracted from the positive reference serum sample, which was the same as that of the commercial real-time PCR kit. The coincidence rate between the two assays was 100%. Conclusions: The LMTIA assay had high sensitivity, good detection, and simple operation. Thus, it is suitable for facilitating preliminary and cost-effective surveillance for the prevention and control of ASFV.

Real-time Smoke Detection Research with False Positive Reduction using Spatial and Temporal Features based on Faster R-CNN

  • Lee, Sang-Hoon;Lee, Yeung-Hak
    • Journal of IKEEE
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    • v.24 no.4
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    • pp.1148-1155
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    • 2020
  • Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

Damage Location Detection of Shear Building Structures Using Mode Shape (모드형상을 이용한 전단형 건물의 손상 위치 추정)

  • Yoo, Suk Hyeong;Lee, Hong Kyu
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.17 no.1
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    • pp.124-132
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    • 2013
  • Damage location and extent could be detected by the inverse analysis on dynamic response of the damaged structure. In general, detection of damage location is possible by the observation of the mode shape difference between undamaged and damaged structure and assessment of stiffness reduction is possible by the observation of the natural frequency difference of them. The study on damage detection by the dynamic response in civil structures is reported enough and in practical use, but in building structures it is reported seldom due to several problems. The purpose of this study is to present the damage detection method on shear building structures by mode shape. The damage location index using 1st mode shape is observed theoretically to find out damage location. The damage detection method is applied to numerical analysis model such as MATLAB and MIDAS GENw for the verification. Finally the shaking table test on 3 story shear building is performed for the examination of the damage detection method. In shaking table results, as the story stiffness decrease by 25% the 1st mode frequency increase by 12%, and the damage location index represents minus at damaged story.

Measured Data based Inspection for Unintended Deflections in Automotive Outer Panels (측정 데이터 이용한 자동차 외판 미세굴곡 추적 사례 연구)

  • Chung, Yun Chan;Lee, Sang Heon;Chang, Dae Soon;Park, Sang Chul
    • Korean Journal of Computational Design and Engineering
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    • v.18 no.2
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    • pp.113-119
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    • 2013
  • This paper proposes an approach to detect unintended deflections in an automotive outer panel. Conventionally, the detection of unintended deflections has been performed by experienced works, and it requires much amount of time and efforts. The motivation of this work is to reduce such efforts by providing an automated detection methodology. For the detection of unintended deflections, we make use of the measured data from an optical scanner which can be considered as a Z-map data. The proposed approach consists of four major steps; 1) measured data acquisition for an automotive outer panel, 2) identification of shape features, 3) removal of shape features, and 4) detection of unintended deflections via curvature analysis.

A Fast Algorithm for Target Detection in High Spatial Resolution Imagery

  • Kim Kwang-Eun
    • Proceedings of the KSRS Conference
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    • 2006.03a
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    • pp.7-14
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    • 2006
  • Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

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Detection of Edge on Radar Image (레이다 영상의 경계 검출)

  • 윤동한;최갑석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.12 no.4
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    • pp.405-413
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    • 1987
  • In this paper, we have discussed three-type median filters(SQUARE, CROSS, X-SHAPE) that preserving edge in an original image while reducing random noise was introduced for image enhancement and edge detection on radar image. Since radar image have a number of parts of curve, we compared results produced by edge detection operater proposed for improving the parts of curve with results of using the existing edge detection methods, such as Roberts, Sobel, Prewitt, Laplacian and Kirsch.

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Object Detection by Gaussian Mixture Model and Shape Adaptive Bidirectional Block Matching Algorithm

  • Park, Goo-Man;Han, Byung-Wan;An, Tae-Ki;Lee, Kwang-Jeek
    • Journal of Broadcast Engineering
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    • v.13 no.5
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    • pp.681-684
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    • 2008
  • We proposed a method to improve moving object detection capability of Gaussian Mixture Model by suggesting shape adaptive bidirectional block matching algorithm. This method achieves more accurate detection and tracking performance at various motion types such as slow, fast, and bimodal motions than that of Gaussian Mixture Model. Experimental results showed that the proposed method outperformed the conventional methods.