• Title/Summary/Keyword: image processing technique

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Stress Analysis of an Edge-Cracked Plate by using Photoelastic Fringe Phase Shifting Method (광탄성프린지 위상이동법을 이용한 에지균열판의 응력 해석)

  • Baek, Tae-Hyun;Kim, Myung-Soo;Cho, Sung-Ho
    • Journal of the Korean Society for Nondestructive Testing
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    • v.20 no.3
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    • pp.213-220
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    • 2000
  • The method of photoelasticity allows one to obtain principal stress differences and principal stress directions in a photoelastic model. In the classical approach, the photoelastic parameters are measured manually point by point. The previous methods require much time and skill in the identification and measurement of photoelastic data. Fringe phase shifting method has been recently developed and widely used to measure and analyze fringe data in photo-mechanics. This paper presents the test results of photoelastic fringe phase shifting technique for the stress analysis of a circular disk under compression and an edge-cracked plate subjected to tensile load. The technique used here requires four phase stepped photoelastic images obtained from a circular polariscope by rotating the analyzer at $0^{\circ}$, $45^{\circ}$, $90^{\circ}$ and $135^{\circ}$. Experimental results are compared with those or FEM. Good agreement between the results can be observed. However, some error may be included if the technique is used to general direction which is not parallel to isoclinic fringe.

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A Feature Point Extraction and Identification Technique for Immersive Contents Using Deep Learning (딥 러닝을 이용한 실감형 콘텐츠 특징점 추출 및 식별 방법)

  • Park, Byeongchan;Jang, Seyoung;Yoo, Injae;Lee, Jaechung;Kim, Seok-Yoon;Kim, Youngmo
    • Journal of IKEEE
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    • v.24 no.2
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    • pp.529-535
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    • 2020
  • As the main technology of the 4th industrial revolution, immersive 360-degree video contents are drawing attention. The market size of immersive 360-degree video contents worldwide is projected to increase from $6.7 billion in 2018 to approximately $70 billion in 2020. However, most of the immersive 360-degree video contents are distributed through illegal distribution networks such as Webhard and Torrent, and the damage caused by illegal reproduction is increasing. Existing 2D video industry uses copyright filtering technology to prevent such illegal distribution. The technical difficulties dealing with immersive 360-degree videos arise in that they require ultra-high quality pictures and have the characteristics containing images captured by two or more cameras merged in one image, which results in the creation of distortion regions. There are also technical limitations such as an increase in the amount of feature point data due to the ultra-high definition and the processing speed requirement. These consideration makes it difficult to use the same 2D filtering technology for 360-degree videos. To solve this problem, this paper suggests a feature point extraction and identification technique that select object identification areas excluding regions with severe distortion, recognize objects using deep learning technology in the identification areas, extract feature points using the identified object information. Compared with the previously proposed method of extracting feature points using stitching area for immersive contents, the proposed technique shows excellent performance gain.

A new approach to enhancement of ground penetrating radar target signals by pulse compression (파형압축 기법에 의한 GPR탐사 반사신호 분해능 향상을 위한 새로운 접근)

  • Gaballah, Mahmoud;Sato, Motoyuki
    • Geophysics and Geophysical Exploration
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    • v.12 no.1
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    • pp.77-84
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    • 2009
  • Ground penetrating radar (GPR) is an effective tool for detecting shallow subsurface targets. In many GPR applications, these targets are veiled by the strong waves reflected from the ground surface, so that we need to apply a signal processing technique to separate the target signal from such strong signals. A pulse-compression technique is used in this research to compress the signal width so that it can be separated out from the strong contaminated clutter signals. This work introduces a filter algorithm to carry out pulse compression for GPR data, using a Wiener filtering technique. The filter is applied to synthetic and field GPR data acquired over a buried pipe. The discrimination method uses both the reflected signal from the target and the strong ground surface reflection as a reference signal for pulse compression. For a pulse-compression filter, reference signal selection is an important issue, because as the signal width is compressed the noise level will blow up, especially if the signal-to-noise ratio of the reference signal is low. Analysis of the results obtained from simulated and field GPR data indicates a significant improvement in the GPR image, good discrimination between the target reflection and the ground surface reflection, and better performance with reliable separation between them. However, at the same time the noise level slightly increases in field data, due to the wide bandwidth of the reference signal, which includes the higher-frequency components of noise. Using the ground-surface reflection as a reference signal we found that the pulse width could be compressed and the subsurface target reflection could be enhanced.

Localization of Unmanned Ground Vehicle based on Matching of Ortho-edge Images of 3D Range Data and DSM (3차원 거리정보와 DSM의 정사윤곽선 영상 정합을 이용한 무인이동로봇의 위치인식)

  • Park, Soon-Yong;Choi, Sung-In
    • KIPS Transactions on Software and Data Engineering
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    • v.1 no.1
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    • pp.43-54
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    • 2012
  • This paper presents a new localization technique of an UGV(Unmanned Ground Vehicle) by matching ortho-edge images generated from a DSM (Digital Surface Map) which represents the 3D geometric information of an outdoor navigation environment and 3D range data which is obtained from a LIDAR (Light Detection and Ranging) sensor mounted at the UGV. Recent UGV localization techniques mostly try to combine positioning sensors such as GPS (Global Positioning System), IMU (Inertial Measurement Unit), and LIDAR. Especially, ICP (Iterative Closest Point)-based geometric registration techniques have been developed for UGV localization. However, the ICP-based geometric registration techniques are subject to fail to register 3D range data between LIDAR and DSM because the sensing directions of the two data are too different. In this paper, we introduce and match ortho-edge images between two different sensor data, 3D LIDAR and DSM, for the localization of the UGV. Details of new techniques to generating and matching ortho-edge images between LIDAR and DSM are presented which are followed by experimental results from four different navigation paths. The performance of the proposed technique is compared to a conventional ICP-based technique.

Arctic Sea Ice Motion Measurement Using Time-Series High-Resolution Optical Satellite Images and Feature Tracking Techniques (고해상도 시계열 광학 위성 영상과 특징점 추적 기법을 이용한 북극해 해빙 이동 탐지)

  • Hyun, Chang-Uk;Kim, Hyun-cheol
    • Korean Journal of Remote Sensing
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    • v.34 no.6_2
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    • pp.1215-1227
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    • 2018
  • Sea ice motion is an important factor for assessing change of sea ice because the motion affects to not only regional distribution of sea ice but also new ice growth and thickness of ice. This study presents an application of multi-temporal high-resolution optical satellites images obtained from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and Korea Multi-Purpose Satellite-3 (KOMPSAT-3) to measure sea ice motion using SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) feature tracking techniques. In order to use satellite images from two different sensors, spatial and radiometric resolution were adjusted during pre-processing steps, and then the feature tracking techniques were applied to the pre-processed images. The matched features extracted from the SIFT showed even distribution across whole image, however the matched features extracted from the SURF showed condensed distribution of features around boundary between ice and ocean, and this regionally biased distribution became more prominent in the matched features extracted from the ORB. The processing time of the feature tracking was decreased in order of SIFT, SURF and ORB techniques. Although number of the matched features from the ORB was decreased as 59.8% compared with the result from the SIFT, the processing time was decreased as 8.7% compared with the result from the SIFT, therefore the ORB technique is more suitable for fast measurement of sea ice motion.

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study (딥러닝 알고리즘을 이용한 저선량 디지털 유방 촬영 영상의 복원: 예비 연구)

  • Su Min Ha;Hak Hee Kim;Eunhee Kang;Bo Kyoung Seo;Nami Choi;Tae Hee Kim;You Jin Ku;Jong Chul Ye
    • Journal of the Korean Society of Radiology
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    • v.83 no.2
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    • pp.344-359
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    • 2022
  • Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.

A Study on Image Reconstruction for Seed Localization for Permanent Prostate Brachytherapy (전립선암 근접치료 시 방사성선원 위치확인을 위한 영상 재구성에 관한 연구)

  • Hong, Ju-Young;Rah, Jeong-Eun;Suh, Tae-Suk
    • Radiation Oncology Journal
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    • v.25 no.2
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    • pp.125-133
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    • 2007
  • [ $\underline{Purpose}$ ]: This study was to design and fabricate a phantom for prostate cancer brachytherapy to validate a developed program applying a 3-film technique, and to compare it with the conventional 2-film technique for determining the location of an implanted seed. $\underline{Materials\;and\;Methods}$: The images were obtained from overlapped seeds by randomly placing a maximum of 63 seeds in the anterior-posterior (AP) position and at $-30^{\circ} to $30^{\circ} at $15^{\circ} intervals. Images obtained by use of the phantom were applied to the image processing procedure, and were then processed into the development program for seed localization. In this study, cases were set where one seed overlapped, where two seeds overlapped and where none of the three views resolved all seeds. The distance between the centers of each seed to the reference seed was calculated in a prescribed region. This distance determined the location of each seed in a given band. The location of the overlapped seeds was compared with that of the 2-film technique. $\underline{Results}$: With this program, the detection rate was 92.2% (at ${\pm}15^{\circ}), 94.1% (at ${\pm}30^{\circ}) and 70.6% (compared to the use of the 2-film technique). The overlaps were caused by one or more than two seeds that overlapped; the developed program can identify the location of each seed perfectly. However, for the third case the program was not able to resolve the overlap of the seeds. $\underline{Conclusion}$: This program can be used to improve treatment outcome for the brachytherapy of prostate cancer by reducing the number of errors in the process of reconstructing the locations of perfectly overlapped seeds.

A New Technique for Improved Positioning Accuracy Employing Gaussian Filtering in Zigbee-based Sensor Networks (지그비 기반의 센서 네트워크에서 Gaussian Filtering 기법을 적용한 위치 추적 향상 기법)

  • Hur, Byoung-Hoe;Kim, Jeong-Gon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.12A
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    • pp.982-990
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    • 2009
  • The IEEE 802.15.4 wireless sensor network is composed of the unique sensor devices to monitor and collect physical or environmental conditions. The interests in a positioning technology, which is one of the environment monitoring technologies, are gradually increased according to the development of the sensor technology and IT infrastructure. Generally, it is difficult for the positioning system using RSSI (Received Signal Strength Indication) based implementation to get accurate position because of obstacles, RF wave's delay and multipath. Therefore, in this paper, we investigate the improved positioning technologies for RSSI-based positioning system. This paper also proposes the enhanced scheme to improve the accuracy of positioning system by applying the Gaussian Filter algorithm, which is widely used for enhancing the performance of image processing system. For the implementation of proposed scheme, we firstly make a look-up tables, which represent the distance between target node and master node and corresponding RSSI value of each target node which are recorded as an average value after investigating the characteristics of attenuation of transmitted signal By applying the pre-determined look-up tables and Gaussian Filtering in the proposed scheme, we analyzed the positioning performance and compared with other conventional RSSI-based positioning algorithms.

Convolution Neural Network Based Auto Classification Model Using Endoscopic Images of Gastric Cancer and Gastric Ulcer (내시경의 위암과 위궤양 영상을 이용한 합성곱 신경망 기반의 자동 분류 모델)

  • Park, Ye Rang;Kim, Young Jae;Chung, Jun-Won;Kim, Kwang Gi
    • Journal of Biomedical Engineering Research
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    • v.41 no.2
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    • pp.101-106
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    • 2020
  • Although benign gastric ulcers do not develop into gastric cancer, they are similar to early gastric cancer and difficult to distinguish. This may lead to misconsider early gastric cancer as gastric ulcer while diagnosing. Since gastric cancer does not have any special symptoms until discovered, it is important to detect gastric ulcers by early gastroscopy to prevent the gastric cancer. Therefore, we developed a Convolution Neural Network (CNN) model that can be helpful for endoscopy. 3,015 images of gastroscopy of patients undergoing endoscopy at Gachon University Gil Hospital were used in this study. Using ResNet-50, three models were developed to classify normal and gastric ulcers, normal and gastric cancer, and gastric ulcer and gastric cancer. We applied the data augmentation technique to increase the number of training data and examined the effect on accuracy by varying the multiples. The accuracy of each model with the highest performance are as follows. The accuracy of normal and gastric ulcer classification model was 95.11% when the data were increased 15 times, the accuracy of normal and gastric cancer classification model was 98.28% when 15 times increased likewise, and 5 times increased data in gastric ulcer and gastric cancer classification model yielded 87.89%. We will collect additional specific shape of gastric ulcer and cancer data and will apply various image processing techniques for visual enhancement. Models that classify normal and lesion, which showed relatively high accuracy, will be re-learned through optimal parameter search.

Analysis of Exposure Doses and Determination of Atmospheric Diffusion Coefficients (피폭선량 해석과 대기확산계수 결정)

  • Kim, Byung-Woo;Han, Moon-Hwee;Lee, Young-Bok;Lee, Jeong-Ho
    • Journal of Radiation Protection and Research
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    • v.9 no.1
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    • pp.26-32
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    • 1984
  • The exposure doses by the radioactive gaseous effluents from nuclear power plants are investigated in the two cases of normal operation and hypothetical accident. Gaussian equation is adapted in the normal operation as the diffusion model of effluents for long period, which uses annual average meteorological data. But the real time models have been used in the case of accidents which analyze the changes of wind direction and speed. In this study the annual exposure doses by the normal operation of Kori unit 1 during $1977{\sim}1982$ were calculated on the basis of the atmospheric diffusion factor by the Gaussian straight line model. And the image processing technique was suggested as the effective method through the wind tunnel experiments to get the characteristic value of atmospheric diffusion coefficient required especially in the accidents of nuclear power plants.

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