• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.681-692
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• 2018

This study was performed to select the significant wavelengths for predicting the chlorophyll content of grafted cucumber seedlings using hyperspectral images. The visible and near-infrared (VNIR) images and the short-wave infrared images of cucumber cotyledon samples were measured by two hyperspectral cameras. A correlation coefficient spectrum (CCS), a stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine significant wavelengths. Some wavelengths at 501, 505, 510, 543, 548, 619, 718, 723, and 727 nm were selected by CCS, SMLR, and PLS as significant wavelengths for estimating chlorophyll content. The results from the calibration models built by SMLR and PLS showed fair relationship between measured and predicted chlorophyll concentration. It was concluded that the hyperspectral imaging technique in the VNIR region is suggested effective for estimating the chlorophyll content of grafted cucumber leaves, non-destructively.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.3
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• pp.130-139
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• 2013

This paper proposes a new approach for enhancing digital images based on red channel information, which has the most analogous characteristics to invisible infrared rays. Specifically, a red channel in RGB space is used to analyze the image contents and improve the visual quality of the input images but it can cause unexpected problems, such as the over-enhancement of reddish input images. To resolve this problem, inter-channel correlations between the color channels were derived, and the weighting parameters for visually pleasant image fusion were estimated. Applying the parameters resulted in significant brightness as well as improvement in the dark and bright regions. Furthermore, simple contrast and color corrections were used to maintain the original contrast level and color tone. The main advantages of the proposed algorithm are 1) it can improve a given image considerably with a simple inter-channel correlation, 2) it can obtain a similar effect of using an extra infrared image, and 3) it is faster than other algorithms compared without artifacts including halo effects. The experimental results showed that the proposed approach could produce better natural images than the existing enhancement algorithms. Therefore, the proposed scheme can be a useful tool for improving the image quality in consumer imaging devices, such as compact cameras.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.515-524
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• 2016

Infrared (IR) wavelength is out of visible range and thus usually cut by hot filters in general commercial cameras. However, some information from the near-IR (NIR) range is known to improve the overall visibility of scene in many cases. For example when there is fog or haze in the scene, NIR image has clearer visibility than visible image because of its stronger penetration property. In this paper, we propose an algorithm for fusing the RGB and NIR images to obtain the enhanced images of the outdoor scenes. First, we construct a weight map by comparing the contrast of the RGB and NIR images, and then fuse the two images based on the weight map. Experimental results show that the proposed method is effective in enhancing visible image and removing the haze.

• Advances in Computational Design
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• v.5 no.2
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• pp.127-146
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• 2020

Building Information Modeling (BIM) is increasingly used throughout the facility's life cycle for various applications, such as design, construction, facility management, and maintenance. For existing buildings, the geometry of as-built BIM is often constructed using dense, three dimensional (3D) point clouds data obtained with laser scanners. Traditionally, as-built BIM systems do not contain the material and textural information of the buildings' elements. This paper presents a semi-automatic method for generation of material and texture rich as-built BIM. The method captures and integrates material and textural information of building elements into as-built BIM using thermal infrared sensing (TIS). The proposed method uses TIS to capture thermal images of the interior walls of an existing building. These images are then processed to extract the interior walls using a segmentation algorithm. The digital numbers in the resulted images are then transformed into radiance values that represent the emitted thermal infrared radiation. Machine learning techniques are then applied to build a correlation between the radiance values and the material type in each image. The radiance values were used to extract textural information from the images. The extracted textural and material information are then robustly integrated into the as-built BIM providing the data needed for the assessment of building conditions in general including energy efficiency, among others.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.546-554
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• 2011

Near targets in the infrared (IR) images have the steady feature for inner region and the transient feature for the boundary region. Based on these features, this paper proposes a new method to extract the fine target shape of near targets in the IR images. First, we detect the boundary region of the candidate targets using the local variance weighted information entropy (WIE) of the original images. And then, a coarse target region can be estimated based on the labeling of the boundary region. For the coarse target region, we use the opening filter with an adaptive structure element to extract the fine target shape. The decision of the adaptive structure element size is optimized for the width information of target boundary by calculating the average WIE in the enlarged windows. The experimental results show that a proposed method has better extraction performance than the previous threshold algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.115.2-115.2
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• 2011

UWISH2 (UKIRT Widefield Infrared Survey for $H_2$) is an unbiased, narrow-band imaging survey of the Galactic plane in the $H_2$ 1-0 S(1) emission line at $2.122{\mu}m$ using the Wide-Field Camera (WFCAM) at the United Kingdom Infrared Telescope (UKIRT). The survey covers about 150 square degrees of the first Galactic quadrant ($10^{\circ}$ < l < $65^{\circ}$; $-1.3^{\circ}$ < b < $+1.3^{\circ}$). The images have a $5{\sigma}$ detection limit of point sources of K~18 mag and the surface brightness limit is $10^{-19}\;W\;m^{-2}$ $arcsec^{-2}$. The survey operation began on 28 July 2009 and has completed on 17 August 2011. We have been studying the supernova remnants (SNRs) in the UWISH2 survey area. Among the known 274 Galactic SNRs, the survey area includes 65 SNRs or 24 percent of the known SNRs. The wide-field and high-quality UWISH2 images allow us to identify both the diffuse extended and compact $H_2$ emission associated with SNRs, which is useful for understanding their physical environment and evolution. The continuum is subtracted from the narrow-band $H_2$ images using the K-band continuum images obtained as part of the UKIDSS GPS (UKIRT Infrared Deep Sky Survey of the Galactic Plane). So far, we have inspected 42 SNRs, and found distinct H2 emission in 14 SNRs. The detection rate is 33%. Some of the SNRs show bright, complex, and interesting structures that have never been reported in previous studies. In this report, we present our identification scheme and preliminary results.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.550-558
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• 2013

The infrared and visible images are represented by different information due to the different wavelength of the light. The infrared image has thermal information and the visible image has texture information. Desirable results are obtained by fusing infrared and visible information. To enhance a visible image, we extract a weight map from a visible image using saturation, brightness. After that, the weight map is adjusted using thermal information in the infrared image. Finally, an enhanced image is resulted from combining an infrared image and a visible image. Our experiment results show that our proposed algorithm is working well to enhance the smoke in the original image.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.711-714
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• 2002

Thermal infrared camera have poor image qualities compared to commercial CCD cameras, as in contrast, brightness, and. resolution. To compensate the poor Image quality problems associated with the thermal infrared camera, the technique of superimposing thermal infrared image into real ccd image is proposed. The mobile robot KAEROT/m2, loaded with sensor head system at the mast, is entered to monitor leakage of heavy water and thermal abnormality of the calandria reactor area in overhaul period. The sensor head system is composed of thermal infrared camera and cod camera In parallel. When thermal abnormality on observation points and areas of calandria reactor area is occurred, unusual hot image taken from thermal infrared camera is superimposed on real CCD image. In this inspection experiment, more accurate positions of thermal abnormalities on calandria reactor area can be estimated by using technique of mapping thermal infrared image into CCD image, which include characters arranged in MPOQ order.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.106-116
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• 2023

In this paper, we designed a coaxial dual camera incorporating two optical systems-one for the visible rays and the other for far-infrared ones-with the aim of capturing images in both wavelength ranges. The far-infrared system, which uses an uncooled detector, has a sensor array of 640×480 pixels. The visible ray system has 1,945×1,097 pixels. The coaxial dual optical system was designed using a hot mirror beam splitter to minimize heat transfer caused by infrared rays in the visible ray optical system. The optimization process revealed that the final version of the dual camera system reached more than 90% of the fusion performance between two separate images from dual systems. Multiple rigorous testing processes confirmed that the coaxial dual camera we designed demonstrates meaningful design efficiency and improved image conformity degree compared to existing dual cameras.