• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.10 no.5
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• pp.43-51
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• 1996

An appropriate lighting standard for the museum is needed to minimize the deterioration of the exhibits by the light. To obtain the basic data for the standard, a system to measure the color change of the artifacts as a function of the radiation energy frequency was constructed. Xenon lamp is used as a light source, and the light is irradiated to the samples through serveral cut-off filters. Measuring the colors of the samples under each filters, color change of the samples is estimated for each frequency ranges of radiation energy. As a result, natural dyes show severe color change in a short time, but traditional papers shows relatively small color change. Using measured results, color change due to other light sources can be calculated and this will be the base of the standard.

• Journal of Sensor Science and Technology
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• v.10 no.5
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• pp.279-285
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• 2001

The capnograph system for determining the partial pressure of carbon dioxide in the blood of a patient was developed based on the NDIR(non-dispersive infrared) absorption technology. NDIR gas analyzing method requires an optical absorption chamber and signal processing hardware. In this paper, we have designed and implemented NDIR type $CO_2$ gas chamber in consideration of the time response characteristics and lamp chopping frequency. And we have implemented signal processing hardware using two infrared sources to reduce the thermal background effect. The implemented gas chamber and signal processing hardware were tested in the temperature variation experiment and human expiratory experiment. The results showed that the system could produce a stable output signal and a good $CO_2$ gas concentration curve like a typical capnogram.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.1-7
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• 2012

Generally, correction methods for faded images use illuminant estimation algorithms, such as the gray world assumption and white patch Retinex methods, as the phenomenon of color fading is regarded as an illuminant effect. However, this induces inaccurate faded color correction, as images fade at different rates according to the ink property, temperature, humidity, and illuminant. Therefore, this paper presents a color correction method for faded images using classification in LCybCrg color space. The input faded image is first separated according to the chromaticity based on LCybCrg opponent color space. The faded color correction is then performed based on the gray world assumption in RGB color space. Thereafter, weights calculated from CybCrg values are applied to reduce contour artifacts. As a result, the proposed method provides better color correction for faded images than previous methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.480-483
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• 2018

In this paper, we study the interference canceling and image sensing processing technology of multiple light sources for high speed transmission in CMOS sensor based visible light communication system. To improve transmission capacity in optical camera communications via image sensors, different data must be transmitted simultaneously from each LED. However, multiple LED light source environments for high-speed transmission can cause interference between adjacent LEDs. In this case, since the visible light communication system generally uses intensity modulation, when a plurality of LEDs transmit data at the same time, it is difficult to accurately detect the respective LEDs due to the light scattering interference of the adjacent LEDs. In order to solve this problem, the ON / OFF state of many LEDs of the light source is accurately recognized by using a dual CMOS sensor, and the spectral estimation technique and the pixel image signal processing technique of each LED are proposed. This technique can accurately recognize multiple LED pixels and improve the total average bit error rate and throughput of a MISO-VLC system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.95-103
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• 1998

This paper proposes an image segmentation algorithm based on fusion of range and intensity images. Based on Bayesian theory, a priori knowledge is encoded by the Markov random field (MRF). A maximum a posteriori (MAP) estimator is constructed using the features extracted from range and intensity images. Objects are approximated by local planar surfaces in range images, and the parametric space is constructed with the surface parameters estimated pixelwise. In intensity images the ${\alpha}$-trimmed variance constructs the intensity feature. An image is segmented by optimizing the MAP estimator that is constructed using a likelihood function based on edge information. Computer simulation results shw that the proposed fusion algorithm effectively segments the images independentl of shadow, noise, and light-blurring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.6
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• pp.624-631
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• 2001

The relationship between the catch of squid, Todarodes pacificus, according to the jigging depth and underwater illumination by fishing lamps was investigated during nighttime operations off-Tusima Islands in November 1994 and off-Oki Islands in November 1995. We used echo sounder to observe the distribution of squid. Echo images on the echo sounder showed the distribution of squid at the water layer of 50 meter depth at the beginning of jigging operation. After the time elapsed, a continuous dense image had moved to the layer of 60-80 meters jigging depth. A larger number of squid were caught by jigging machines set at a lowest depth of 90 meters, when it compared with machines set at a 60 meters. However, Catch increased around 60 meters jigging depth, when fishing lamps output were switched to 24 kW halogen lights:.The underwater illumination, under the each light power of fishing lamps of squid jigging boat was continuously measured with an underwater illuminometer. Values of the underwater illumination, when schools were distributed from 60 to 80 meters, ranged from $3.0\times10^{-2}lx\;to\;3.4\times10^{-3}lx$ in average at $80\~360$ kW fishing lamps output of squid jigging boat.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• Korean Journal of Optics and Photonics
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• v.14 no.4
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• pp.473-479
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• 2003

The purpose of this study is to recover the CIE XYZ tristimulus values of original colors from scanner output signals, and to reproduce true colors on the monitor. The process of this study is composed of three steps; scanner characterization, chromatic adaptation transformation, and color space transformation between and sRGB. Especially, in the process of recovery, scanner stimuli were obtained accurately by dividing the non-linear photometric response curve into two parts. As the result of test to EPSON Expression 1680 scanner, the average color difference between true and recovered XYZ for 228 target colors, 22 test neutrals, and 36 test colors were 1.49, 0.97, and 1.42 $\Delta$ $E_{UV}$ *, respectively. With the transformation from illuminant D50 to illuminant D65, the input signals to sRGB monitor were predicted. Finally, it could be found that displayed colors with predicted input signals were very consistent with true colors. with true colors.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.24-35
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• 2016

By estimating environment light distribution, we can generate realistic shadow images in AR(augmented reality). When we estimate light distribution without sensing equipment, environment light model, geometry of virtual object, and surface reflection property are needed. Previous study using 3D marker builds surrounding light environment with a geodesic dome model and analyzes shadow images. Because this method employs candidate shadow maps in initial scene setup, however, it is difficult to estimate precise light information. This paper presents a novel light estimation method based on hierarchical light distribution model subdivision. By using an overlapping area ratio of the segmented shadow and candidate shadow map, we can make hierarchical subdivision of light geodesic dome.