• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2361-2369
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• 2020

Compton cameras using large scintillators have been developed for high imaging sensitivity. These scintillator-based Compton cameras, however, mainly due to relatively low energy resolution, suffer from undesired background-radiation signals, especially when radioactive materials' activity is very low or their location is far from the Compton camera. To alleviate this problem for a large-size Compton camera, in the present study, a hybrid-type shielding system was designed that combines an active shield with a veto detector and a passive shield that surrounds the active shield. Then, the performance of the hybrid shielding system was predicted, by Monte Carlo radiation transport simulation using Geant4, in terms of minimum detectable activity (MDA), signal-to-noise ratio (SNR), and image resolution. Our simulation results show that, for the most cases, the hybrid shielding system significantly improves the performance of the large-size Compton camera. For the cases investigated in the present study, the use of the shielding system decreased the MDA by about 1.4, 1.6, and 1.3 times, increased the SNR by 1.2-1.9, 1.1-1.7, and 1.3-2.1 times, and improved the image resolution (i.e., reduced the FWHM) by 7-8, 1-6, and 3-5% for ¹³⁷Cs, ⁶⁰Co, and ¹³¹I point source located at 1-5 m from the imaging system, respectively.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3700-3708
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• 2023

The stochastic origin ensembles method with resolution recovery (SOE-RR) has been proposed to reconstruct proton-induced prompt gammas (PGs), and the reconstructed PG image was used for range verification. However, due to low detection efficiency, the number of valid events is low. Such a low-count condition can degrade the accuracy of the SOE-RR method for proton range verification. In this study, we proposed two strategies to improve the reconstruction of the SOE-RR algorithm for low-count PG imaging. We also studied the number of iterations and repetitions required to achieve reliable range verification. We simulated a proton beam (10⁸ protons) irradiated on a water phantom and used a two-layer Compton camera to detect 4.44-MeV PGs. Our simulated results show that combining the SOE-RR algorithm with restricted volume (SOE-RR-RV) can reduce the error of the estimation of the Bragg peak position from 5.0 mm to 2.5 mm. We also found that the SOE-RR-RV algorithm initialized using a back-projection image could improve the convergence rate while maintaining accurate range verification. Finally, we observed that the improved SOE-RR algorithm set for 60,000 iterations and 25 repetitions could provide reliable PG images. Based on the proposed reconstruction strategies, the SOE-RR algorithm has the potential to achieve a positioning error of 2.5 mm for low-count PG imaging.

• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.300-305
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• 2018

The uncooled microbolometer thermal sensor for low cost and mass volume was designed to target the new infrared market that includes smart device, automotive, energy management, and so on. The microbolometer sensor features 80x60 pixels low-resolution format and enables the use of wafer-level vacuum packaging (WLVP) technology. Read-out IC (ROIC) implements infrared signal detection and offset correction for fixed pattern noise (FPN) using an internal digital to analog convertor (DAC) value control function. A reliable WLVP thermal sensor was obtained with the design of lid wafer, the formation of Au80%wtSn20% eutectic solder, outgassing control and wafer to wafer bonding condition. The measurement of thermal conductance enables us to inspect the internal atmosphere condition of WLVP microbolometer sensor. The difference between the measurement value and design one is $3.6{\times}10-9$ [W/K] which indicates that thermal loss is mainly on account of floating legs. The mean time to failure (MTTF) of a WLVP thermal sensor is estimated to be about 10.2 years with a confidence level of 95 %. Reliability tests such as high temperature/low temperature, bump, vibration, etc. were also conducted. Devices were found to work properly after accelerated stress tests. A thermal camera with visible camera was developed. The thermal camera is available for non-contact temperature measurement providing an image that merged the thermal image and the visible image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.36-47
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• 1992

In this paper, a set of TM(template matching) mask using the AM(associative mapping) technique was generated and the edge detection algorithm for a SAR image was proposed. And also, the performance of the proposed edge detection algorithm was tested with the conventional edge detection techniques. The proposed edge detection algorithm created an edge image which was more accurate and clear than the conventional edge detection techniques and the performance of the proposed detection technique was not deteriorated for low intensity area in the image because the uncertainly thresholded value genetated by the conventional detection methods was requested. Also, the number of masks and the detection time were reduced by adjusting resolution of edge detection and the consideration for the threshold value extracting the edge was very intuitive.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.38-41
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• 2002

Mammography is considered the single most important diagnostic tool in the early detection of breast cancer. Today's dedicated mammographic equipment, specially designed x-ray screen/film combinations, coupled with controlled film processing, produces excellent image quality and can detect very low contrast small lesions. In mammography, it is most important to produce consistent high-contrast, high-resolution images at the lowest radiation dose consistent with high image quality. Some of the major technical development milestones that have let to today's high quality in mammographic imaging are reviewed. Both the American College of Radiology Mammography Accreditation Program and the Mammography Quality Standards Act have significant impact on the improvement of the technical quality of mammographic images in the United States and worldwide. A most recent development in digital mammography has opened up avenues for improving diagnosis.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1022-1024
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• 2009

Stereoscopic Images provide depth information with the relative distances between the objects in the images. There are many different ways to extract disparity maps from the visible spectral images. For the infrared spectral range, the same approach cannot be utilized for the innate low resolution and colorless features because typical methods require corresponding features between the images. The authors suggest a new approach that makes use of image segmentation to obtain depth information for stereoscopic millimeter-wave images. For image segmentation a selective visual attention model based on the theory of a feature-integration of attention is used. Experimental results show the proposed method provides reasonable depth information for object shape recognition and display.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.578-578
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• 2002

Thermal infrared imaging is a highly promising technology for condition monitoring and predictive maintenance of electronic, electrical and mechanical elements in nuclear power plants. However, conventional low-cost infrared imaging systems suffer from poor spatial resolution compared to commercial CCD cameras. This paper describes an approach to enhance inspection performances for calandria reactor area of Wolsung nuclear power plant through the technique of superimposing thermal infrared image into real CCD image. In the occurrence of thermal abnormalities on observation points and areas of calandria reactor area, unusual hot image taken from thermal infrared camera is mapped upon real CCD image. The performance of the technique has been evaluated in the experiment carried out at Wolsung nuclear power plant in the overhaul period. The results show that localizations of thermal abnormalities on calandria reactor face can be estimated accurately.

• Journal of IKEEE
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• v.26 no.3
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• pp.475-482
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• 2022

Signal pre-processing and post-processing are some areas of study around electrical resistance tomography due to the low spatial resolution of pixel-based reconstructed images. In addition, methods that improve integrity and noise reduction are candidates for application in ERT. Lately, formulations of image processing methods provide new implementations and studies to improve the response against noise. For example, compact variational mode decomposition has recently shown good performance in image decomposition and segmentation. The results from this first approach of C-VMD to ERT show an improvement due to image segmentation, providing filtering of noise in the background and location of the target.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1453-1461
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• 2015

Since digital images and videos are rapidly increasing in the internet with the spread of mobile computers and smartphones, research on image retrieval has gained tremendous momentum. Color, shape, and texture are major features used in image retrieval. Especially, color information has been widely used in image retrieval, because it is robust in translation, rotation, and a small change of camera view. This paper proposes a new method for histogram refinement based on local color difference. Firstly, the proposed method converts a RGB color image into a HSV color image. Secondly, it reduces the size of color space from 256³ to 32. It classifies pixels in the 32-color image into three groups according to the color difference between a central pixel and its neighbors in a 3x3 local region. Finally, it makes a color difference vector(CDV) representing three refined color histograms, then image retrieval is performed by the CDV matching. The experimental results using public image database show that the proposed method has higher retrieval accuracy than other conventional ones. They also show that the proposed method can be effectively applied to search low resolution images such as thumbnail images.