• Journal of Environmental Impact Assessment
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• v.22 no.5
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• pp.513-523
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• 2013

This purpose of this work is to explore the characteristics of urban thermal environment distribution with the case of Daejeon. To do that, this work applied GIS Spatial Statistics to the LandSAT images gathered from 2000 to 2011. The urban thermal environment distribution at the time point of 2 showed high spatial autocorrelation. Therefore, it is judged that spatial autocorrelation is needed to increase the reliability and explanatory power of the characteristics of thermal environment distribution. In the case of the thermal in Daejeon, its positive clustering appeared high at the time point of 2, and its clustering in 2011 more gradually decreased than that in 2000 to 2011. In particular, given the decrease in the core H-H region, it was found that the thermal environment of Daejeon was greatly improved. However, since the rise in the region L-L means another changed like construction of a new city, it is judged that it is necessary to come up with a proper plan. It is considered that this analysis of the characteristics of urban thermal environment distribution in consideration of spatial autocorrelation L-L be useful for providing a fundamental material necessary for the policy and project of thermal environment improvement.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.278-283
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• 1999

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.431-438
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• 1999

The environmental control based on interactive thermoregulatory behavior for swine production has many advantages over the conventional temperature-based control methods. Therefore, this study was conducted to compare various feature selection methods using postural images of growing pigs under various environmental conditions. A color CCD camera was used to capture the behavioral images which were then modified to binary images. The binary images were processed by thresholding, edge detection, and thinning techniques to separate the pigs from their background. Following feature were used for the input patterns to the neural network ; \circled1 perimeter, \circled2 area, \circled3 Fourier coefficients (5$\times$5), \circled4 combination of (\circled1 ＋ \circled2), \circled5 combination of (\circled1 ＋ \circled3), \circled6 combination of (\circled2 ＋ \circled3), and \circled7 combination of (\circled1 ＋ \circled2 ＋ \circled3). Using the above each input pattern, the neural network could classify training images with the success rates of 96%, 96%, 96%, 100%, 100%, 96%, 100%, and testing images with those of 88%, 86%, 93%, 96%, 91%, 90%, 98%, respectively. Thus, the combination of perimeter, area and Fourier coefficients of the thinning images as neural network features gave the best performance (98%) in the behavioral classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.361-368
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• 2018

As autonomous driving research is rapidly developing, pedestrian detection study is also successfully investigated. However, most of the study utilizes color image datasets and those are relatively easy to detect the pedestrian. In case of color images, the scene should be exposed by enough light in order to capture the pedestrian and it is not easy for the conventional methods to detect the pedestrian if it is the other case. Therefore, in this paper, we propose deep neural network (DNN)-based multi-spectrum pedestrian detection method using color and thermal images. Based on single-shot multibox detector (SSD), we propose fusion network structures which simultaneously employ color and thermal images. In the experiment, we used KAIST dataset. We showed that proposed SSD-H (SSD-Halfway fusion) technique shows 18.18% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 2.1% lower miss rate compared to the conventional halfway fusion method.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.738-747
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• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.

• Journal of the Korean institute of surface engineering
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• v.35 no.5
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• pp.325-329
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• 2002

We investigated the oxide thickness and color evolution with the oxidation temperatures between $370^{\circ}C$ and $950^{\circ}C$ for 30 minutes in an electric furnace. Oxide thickness and color index were determined by cross sectional field emission scanning electron microscopy (FESEM) images and digital camera images, respectively. We confirmed that thermal oxidation was suitable for the mass production of color-titanium products, while coloring process window was narrow compared with anodizing oxidation process.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.880-881
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• 2018

Recently technological advancements have allowed visible, infrared and thermal imaging systems to be readily available for security and access control. Increasing applications of facial recognition for security and access control leads to emerging spoofing methodologies. To overcome these challenges of occlusion, replay attack and disguise, researches have proposed using multiple imaging modalities. Using infrared and thermal modalities alongside visible imaging helps to overcome the shortcomings of visible imaging. In this paper we review and propose hybrid feature extraction methods to combine data from multiple imaging systems simultaneously.

• Korean Journal of Remote Sensing
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• v.3 no.2
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• pp.81-87
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• 1987

The study was to analysis the dispersion of the cooling water of Kori atomic power station using thermal infrared data. The dispersion pattern of the cooling water analysis clearly on the LANDSAT TM band 6. It was changed due to tidal current, that is, the cooling water disperses north-eastern direction during the low tide and southweatern direction during the high tide. The relative temperature distribution was mapped through the density slicing method on the images.

• Journal of Institute of Convergence Technology
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• v.10 no.1
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• pp.13-17
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• 2020

Among the factors that must be taken into account when using thermal imaging cameras that are expanding their application to various fields, a basic study was conducted focusing on temperature on the effect of solar radiation on the photographed thermal image. Through all experiments, in order to use an image taken with a thermal imaging camera for an object installed or located outdoors, a separate temperature correction according to the size of solar radiation or a separate device to block the effect of solar radiation must be additionally installed. Since the temperature of the same object may vary in the thermal image taken indoors or outdoors, it is necessary to calibrate it through comparison with other temperatures as a reference point. In the case of measuring the temperature of a glossy surface such as metal indoors with a thermal imaging camera, it was confirmed that an environment that can remove the light reflection effect by the glossy surface must be constructed and photographed.