• Korean Journal of Optics and Photonics
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• v.29 no.6
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• pp.268-274
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• 2018

In this study we suggest a humidity-sensitive color sensor using a one-dimensional photonic crystal and Hong Kong University of Science and Technology-1 (HKUST-1), which is a metal-organic framework (MOF) substance. One-dimensional photonic crystals have a photonic band gap, due to a periodic refractive-index change, and block and reflect light components in a specific wavelength band. The refractive index of HKUST-1 differs in dry and humid environments. Herein we designed a sensor using the presence of the photonic band gap, with FDTD simulation. As a result of optical analysis, the color conversion of the reflected light was superior to the color conversion of the transmitted light. When the center wavelength of the photonic band gap was 550 nm, the maximum peak value of the wet environment increased by a factor of about 9.5 compared to the dry environment, and the color conversion from achromatic to green was excellent as a sensor. The results of this study suggest the application of MOF materials to moisture sensors, and the nanostructure design of MOF materials will expand the applications to industrial devices.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.1-16
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• 1996

Korea Aerospace Research Institute(KARI) is developing a Korea Multi-Purpose Satellite I(KOMPSAT-I) which accommodates Electro-Optical Camera(EOC), Ocean Color Imager(OCI), Space Physics Sensor(SPS) for cartography, ocean color monitoring, and space environment monitoring respectively. The satellite has the weight of about 500 kg and is operated on the sun synchronized orbit with the altitude of 685km, the orbit period of 98 minutes, and the orbit revisit time of 28days. The satellite will be launched in the third quarter of 1999 and its lifetime is more than 3 years. EOC has cartography mission to provide images for the production of scale maps, including digital elevation models, of Korea from a remote earth view in the KOMPSAT orbit. EOC collects panchromatic imagery with the ground sample distance(GSD) of 6.6m and the swath width of 15km at nadir through the visible spectral band of 510-730 nm. EOC scans the ground track of 800km per orbit by push-broom and body pointed method. OCI mission is worldwide ocean color monitoring for the study of biological oceanography. OCI is a multispectral imager generating 6 color ocean images with and <1km GSD by whisk-broom scanning method. OCI is designed to provide on-orbit spectral band selectability in the spectral range from 400nm to 900nm. The color images are collected through 6 primary spectral bands centered at 443, 490, 510, 555, 670, 865nm or 6 spectral bands selected in the spectral range via ground commands after launch. SPS consists of High Energy Particle Detector(HEPD) and Ionosphere Measurement Sensor(IMS). HEPD has mission to characterize the low altitude high energy particle environment and to study the effects of radiation environment on microelectronics. IMS measures densities and temperature of electrons in the ionosphere and monitors the ionospheric irregularities in KOMPSAT orbit.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.634-638
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• 2018

Haze is a factor that degrades the performance of various image processing algorithms, such as those for detection, tracking, and recognition using an electro-optical sensor. For robust operation of an electro-optical sensor-based unmanned system used outdoors, an algorithm capable of effectively removing haze is needed. As a haze removal method using a single electro-optical sensor, the dark channel prior using statistical properties of the electro-optical sensor is most widely known. Previous methods used a square filter in the process of obtaining a transmission using the dark channel prior. When a square filter is used, the effect of removing haze becomes smaller as the size of the filter becomes larger. When the size of the filter becomes excessively small, over-saturation occurs, and color information in the image is lost. Since the size of the filter greatly affects the performance of the algorithm, a relatively large filter is generally used, or a small filter is used so that no over-saturation occurs, depending on the image. In this paper, we propose an improved haze removal method using color image segmentation. The parameters of the color image segmentation are automatically set according to the information complexity of the image, and the over-saturation phenomenon does not occur by estimating the amount of transmission based on the parameters.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.353-356
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• 2019

The main functionality of occupancy sensors is to determine the existence of humans in the space. If the space is occupied, a light is on and for vacancy, the light automatically turns off. In this letter, the functionality is realized by the utilization of color information. The color information of incoming people is saved. For outgoing people, their color distribution is compared with the saved information, thus providing the recognition of the outgoing people. For the comparison, four similarity metrics are examined to validate the proposed method.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.14-22
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• 2014

An autonomous vehicle requires a technology of generating maps by recognizing surrounding environment. The recognition of the vehicle's environment can be achieved by using distance information from a 2D laser scanner and color information from a camera. Such sensor information is used to generate 2D or 3D maps. A 2D map is used mostly for generating routs, because it contains information only about a section. In contrast, a 3D map involves height values also, and therefore can be used not only for generating routs but also for finding out vehicle accessible space. Nevertheless, an autonomous vehicle using 3D maps has difficulty in recognizing environment in real time. Accordingly, this paper proposes the technology for generating 2D maps that guarantee real-time recognition. The proposed technology uses only the color information obtained by removing height values from 3D maps generated based on the fusion of 2D laser scanner and camera data.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Journal of Information Display
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• v.13 no.1
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• pp.7-16
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• 2012

In this paper, the problem concerning the color accuracy of imaging systems using color filters is examined. It is shown that the only solution to the problem is to build systems with the spectral response matching the CIE curves as closely as possible. If the spectral response does not closely match the CIE curves, it was demonstrated that calibration cannot solve the problem and will result in very unstable colorimeters. A practical solution that uses telecentric lenses on the sensor side in addition to dedicated color filters for each CCD detector is presented. For systems that closely match the CIE curves, an innovative method of improving the color accuracy based on the precise measurement of the spectral response is presented. The small discrepancies in the spectral response with regard to the CIE curves are corrected in different ways during the measurements. Finally, it is shown that the tristimulus calibration that is used for display measurement is very unstable for systems without CIE matching and is much more stable with systems that closely match the CIE curves.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.379-383
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• 2015

This paper presents an approach to detect object distance with the use of the recently developed low-cost Kinect sensor. The technique is based on Kinect color depth-image processing and can be used to design various computer-vision applications, such as object recognition, video surveillance, and autonomous path finding. The proposed technique uses keypoint feature detection in the Kinect depth image and advantages of depth pixels to directly obtain the feature distance in the depth images. This highly reduces the computational overhead and obtains the pixel distance in the Kinect captured images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.437-445
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• 2009

Node of USN(Ubiquitous Sensor Network) environments uses radio based communication mechanism for a delivering message. Thus the multiple hop flooding is necessary for considering limited power and communicating unknown node long-range. However if there are many unknown nodes located in users' vision, the flooding of the radio base is insufficient alternative. Thus as a flash may be used to find a key in a darkness, a more direct and short range communication system is required. This study shall resolve such question through 2 different steps. First a Two kind of code method that convert Text message to color code shall be suggested. Second a hardware module, which may deliver various LED(light-emitting diode) color signal, shall be developed. The experiment may take place by fixing the entry angle of light and distance by setting a specific element as a standard mean.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.90-99
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• 2023

In this paper, we propose a super resolution network for raw sensor image which data size is lower comparatively to RGB image. But the actual capabilities of raw image super resolution depends on color correction because its absent of camera post processing that leads to unintended result having different white balance, saturation, etc. Thus, we introduce novel color corrector attention network by adopting the idea of precedent raw super resolution research, and tune to the our faced problem from data specification. The result is not superior to former researches but shows decent output on certain performance matrix. In the same time, we encounter new challenging problem of unexpected shadowing artifact around image objects that cause performance declination despite its good result overall. This problem remains a task to be solved in the future research.