• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.123-130
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• 2016

Purpose : The aim of this study was to compare the differences between the volumes acquired with four-dimensional computed tomography (4DCT)images with a reconstruction image-filtering algorithm and cone-beam computed tomography (CBCT) images with dynamic phantom. Materials and Methods : The 4DCT images were obtained from the computerized imaging reference systems (CIRS) phantom using a computed tomography (CT) simulator. We analyzed the volumes for maximum intensity projection (MIP), minimum intensity projection (MinIP) and average intensity projection (AVG) of the images obtained with the 4DCT scanner against those acquired from CBCT images with CT ranger tools. Results : Difference in volume for node of 1, 2 and 3 cm between CBCT and 4DCT was 0.54~2.33, 5.16~8.06, 9.03~20.11 ml in MIP, respectively, 0.00~1.48, 0.00~8.47, 1.42~24.85 ml in MinIP, respectively and 0.00~1.17, 0.00~2.19, 0.04~3.35 ml in AVG, respectively. Conclusion : After a comparative analysis of the volumes for each nodal size, it was apparent that the CBCT images were similar to the AVG images acquired using 4DCT.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.101-109
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• 2014

It is an essential element for the establishment of image processing related systems to find the exact contour from the image of an arbitrary object. In particular, if a vision system is established to inspect the products in the automated production process, it is very important to detect the contours for standardized shapes such lines and curves. In this paper, we propose a prototype adaptive dynamic contour model, p-Snake with improved contour extraction algorithms by adding the prototype energy. The proposed method is to find the initial contour by applying the existing Snake algorithm after Sobel operation is performed for prototype analysis. Next, the final contour of the object is detected by analyzing prototypes such as lines and circles, defining prototype energy and using it as an additional energy item in the existing Snake function on the basis of information on initial contour. We performed experiments on 340 images obtained by using an environment that duplicated the background of an industrial site. It was found that even if objects are not clearly distinguished from the background due to noise and lighting or the edges being insufficiently visible in the images, the contour can be extracted. In addition, in the case of similarity which is the measure representing how much it matches the prototype, the prototype similarity of contour extracted from the proposed p-ACM is superior to that of ACM by 9.85%.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.339-349
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• 2018

The massive green tide occurred every summer in the Yellow Sea since 2008, and many studies are being actively conducted to estimate the coverage of green tide through analysis of satellite imagery. However, there is no satellite images selection criterion for accurate coverage calculation of green tide. Therefore, this study aimed to find a suitable satellite image from for the comparison of the green tide coverage according to the spatial resolution of satellite image. In this study, Landsat ETM+, MODIS and GOCI images were used to coverage estimation and its spatial resolution is 30, 250 and 500 m, respectively. Green tide pixels were classified based on the NDVI algorithm, the difference of the green tide coverage was compared with threshold value. In addition, we estimate the proportion of the green tide in one pixel through the Linear Spectral Unmixing (LSU) method, and the effect of the difference of green tide ratio on the coverage calculation were evaluated. The result of green tide coverage from the calculation of the NDVI value, coverage of green tide usually overestimate with decreasing spatial resolution, maximum difference shows 1.5 times. In addition, most of the pixels were included in the group with less than 0.1 (10%) LSU value, and above 0.5 (50%) LSU value accounted for about 2% in all of three images. Even though classified as green tide from the NDVI result, it is considered to be overestimated because it is regarded as the same coverage even if green tide is not 100% filled in one pixel. Mixed-pixel problem seems to be more severe with spatial resolution decreases.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.117-125
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• 2008

The Object tracking by real-time image analysis is one of the major concerns in computer vision and its application fields. The Object detection process of real-time images must be preceded before the object tracking process. To achieve the stable object detection performance in the exterior environment, adaptive background model generation methods are needed. The adaptive background model can accept the nature's phenomena changes and adapt the system to the changes such as light or shadow movements that are caused by changes of meridian altitudes of the sun. In this paper, we propose a robust background model generation method effective in an illegal parking auto-detection application area. We also provide a evaluation method that judges whether a moving vehicle stops or not. As the first step, an initial background model is generated. Then the differences between the initial model and the input image frame is used to trace the movement of object. The moving vehicle can be easily recognized from the object tracking process. After that, the model is updated by the background information except the moving object. These steps are repeated. The experiment results show that our background model is effective and adaptable in the variable exterior environment. The results also show our model can detect objects moving slowly. This paper includes the performance evaluation results of the proposed method on the real roads.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.117-129
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• 2021

The movement of animals is one of the primary factors that influence the variation of livestock emissions. This study evaluated the relationship between animal activity and three major emissions, PM₁₀, PM_2.5, and ammonia gas, in weaning, growing, and fattening pig houses through continuous monitoring of the animal activity. The movement score of animals was quantified by the developed image analysis algorithm using 10-second video clips taken in the pig houses. The calculated movement scores were validated by comparison with six activity levels graded by an expert group. A comparison between PMs measurement and the movement scores demonstrated that an increase of the PMs concentrations was obviously followed by increased movement scores, for example, when feeding started. The PM₁₀ concentrations were more affected by the animal activity compared to the PM_2.5 concentrations, which were related to the inflow of external PM_2.5 due to ventilation. The PM₁₀ concentrations in the fattening house were 1.3 times higher than those in the weaning house because of the size of pigs while weaning pigs were more active and moved frequently compared to fattening pigs showing 2.45 times higher movement scores. The results also indicated that indoor ammonia concentration was not significantly influenced by animal activity. This study is significant in the sense that it could provide realistic emission factors of pig farms considering animal's daily activity levels if further monitoring is carried out continuously.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• Journal of radiological science and technology
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• v.44 no.2
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• pp.91-100
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• 2021

High-Resolution thoracic CT (HRCT) is a scanning protocol in which thin slice thickness and sharpness algorithm are utilized to enhance image resolution for diagnosis and assessment of interstitial lung disease (ILD). This examination is sometimes performed in both supine and prone position to improve sensitivity to early changes of these conditions. Anatomical structures (the size of lung field and heart and descending aorta) of 150 patients who underwent HRCT were retrospectively compared. HRCT had been conducted in two positions (supine and prone). Data were divided into five groups according to patient body weights (from 40 to more than 80kg, 10kg intervals, 60 patients/each group). Quantitative analysis was utilized in Image J program. In the supine position defined as the control group, the average values of lung fields and heart size and aorta were compared with the prone position defined as the experimental group. The size of the lungs was found to be higher in the supine position, and it was confirmed that there was a statistically significant difference in patients over 70 kg (p<0.05). In addition, both sizes of the heart and descending aorta were larger in prone position, but in the case of the heart, there was no correlation with the presence or absence of ILD disease (p>0.05). Also, the area of prone in the descending aorta was higher than supine position, but there was no statistically significant difference between supine and prone position (p>0.05). In conclusion, when the severity of ILD disease was severe, there was no statistically significant difference in the area difference between supine and prone position, so it is considered that it will be helpful in diagnostic decision.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Conservation Science
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• v.38 no.2
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• pp.154-165
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• 2022

The color appearance of cultural heritage are essential factors for manufacturing technique interpretation, conservation treatment usage, and condition monitoring. Therefore, this study systematically established color reproduction procedures based on the digital color management system for the portrait of Gwon Eungsu. Moreover, various application strategies for recording and conserving the cultural heritage were proposed. Overall color reproduction processes were conducted in the following order: photography condition setting, standard color measurements, digital photography, color correction, and color space creation. Therefore, compared with the color appearance, the digital image applied to a camera maker profile indicated an average color difference of 𝜟10.1. However, the digital reproduction result based on the color management system exhibits an average color difference of 𝜟1.1, which is close to the color appearance. This means that although digital photography conditions are optimized, recording the color appearance is difficult when relying on the correction algorithm developed by the camera maker. Therefore, the digital color reproduction of cultural heritage is required through color correction and color space creation based on the raw digital image, which is a crucial process for documenting the color appearance. Additionally, the recording of color appearance through digital color reproduction is important for condition evaluation, conservation treatment, and restoration of cultural heritage. Furthermore, standard data of imaging analysis are available for discoloration monitoring.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.