• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.13 no.2
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• pp.163-168
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• 1995

There are four types of color model to repesent color, which are RGB, IHS, CMY, and YIQ color model. RGB color model is the designation of the digital numbers(DNs) of the three primary colors(red, green, and blue), which are used to produce color images on color monitors. IHS color model is the designation of in-tensity, hue, and saturation(IHS). An advantage of considering color in terms of IHS over that of RGB is arrives more easily at a desired color product mathematically. In this study, authors use the IHS transformation and in-tensity-adjustment method to produce the color map images with Landsat TM and scanned map image. And, authors suggest the problems and their solutions when users produce the desired new images with satellite images and map images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.383-386
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• 2002

It is very important to inspect color of printed texture in the textile process. To distinguish the color of the printed texture, RGB color values obtained from a scanner must be transformed to the standard colorimetric system used in the textile industry. It is XYZ color system that is defined by CIE(Commission Internationale do 1Eclairage). The mapping from RGB to XYZ color values is not simple and the scanner has even a positional deviation of RGB colors. In this paper an automatic color inspection method using a general scanning machine is presented. We used a U(neural network) model to map RGB to XYZ and compensate the positional error. In the real experiments, this inspection system shows to get very exact XYZ values from the traditional scanner regardless of the measuring position.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4472-4477
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• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.70-75
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• 2003

It is very important to inspect the color of printed texture in the textile process. The standard colorimetric system used for the recognition of the color in the textile industry. It uses XYZ color system defined by CIE (Commission Internationale de 1Eclairage), but is too expensive. Therefore, in this paper, we propose a color inspection system of the printed texture using a color scanner. Because the scanner uses RGB value for color, it is necessary the mapping from RGB to XYZ. However, the mapping is not simple, and the scanner has even positional deviation because of the geometric characteristics. To transform from RGB to XYZ, we used a NN (neural network) model and also compensated the positional deviation. In real experiments, we could get fairly exact XYZ value from the proposed color inspection system in spite of using a color scanner with large measuring area.

• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.1
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• pp.11-26
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• 2007

In this paper, an experiment was done where the input device used the linear multiple regression and the sRGB color space to perform a color transformation. The output device used the GOG, GOGO and sRGB for the color transformation. After the input device underwent a color transformation, a $3\;{\times}\;20\;size$ matrix was used in a linear multiple regression and the scanner's color representation of scanner was better than a digital still camera's color representation. When using the sRGB color space, the original copy and the output copy had a color difference of 11. Therefore it was more efficient to use the linear multiple regression method than using the sRGB color space. After the input device underwent a color transformation, the additivity of the LCD monitor's R, G and B signal value improved and therefore the error in the linear formula transformation decreased. From this change, the LCD monitor with the GOG model applied to the color transformation became better than LCD monitors with other models applied to the color transformation. Also, the color difference varied more than 11 from the original target in CRT and LCD monitors when a sRGB color transformation was done in restricted conditions.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.335-344
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• 2018

The power model is needed to handle the power consumption of mobile AMOLED display at the software level. However, the existing studies to generate the power model have required the experimental environment and equipment for the power measurement activity. In addition, the combination of RGB values used for modeling was imprudent and small, so it was difficult to reflect the mutual influence between the RGB values into the model. To solve these problems, we propose an RGB color space partitioning method, which is used to prudently sample the combinations of the RGB values based on the color or the power. We also propose a process for generating a mapping table composed of . We analyzed the characteristics of the samples generated according to the proposed partitioning methods, taking into account the color and the power, and generated the mapping table for the AMOLED display. Furthermore, we confirmed the reusability of the mapping table by utilizing one mapping table multiple times in evaluating different power models. These mapping tables are provided to researchers and can be used to generate and evaluate power models without power measurement activities.

• Science of Emotion and Sensibility
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• v.15 no.3
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• pp.381-388
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• 2012

The purpose of this study is to check if LED lighting can be used as general lighting and examine the color rendering property of full color RGB LED lighting. CRI is one of the important properties of evaluating lighting. However the present CRI does not fully evaluate LED lightings. Firstly, the performance of a simple task was compared other than comparing CRI values for different lighting. For experimental preparation three types of lightings were used; standard D65 fluorescent tube, general household fluorescent tube, and RGB LED lighting. All three lightings show high error for Purple-Red. All three lightings show similar error for all hues and prove that color discrimination is not affected by the lighting. This proves that LED could be used as general lighting. Secondly, problems of the conventional CIE CRI method are considered and new models are suggested for the new lighting source. Each of the models was evaluated with visual experiment results obtained by the white light matching experiment. The suggested model is based on the CIE CRI method but replaces the color space model by CIELAB, color difference model by CIEDE2000, and chromatic adaptation model by CAT02.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1603-1613
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• 2017

This paper proposes a method for effectively segmenting the hand region using a widely popular RGB color-based webcam. This performs the empirical preprocessing method four times to remove the noise. First, we use Gaussian smoothing to remove the overall image noise. Next, the RGB image is converted into the HSV and the YCbCr color model, and global fixed binarization is performed based on the statistical value for each color model, and the noise is removed by the bitwise-OR operation. Then, RDP and flood fill algorithms are used to perform contour approximation and inner area fill operations to remove noise. Finally, ROI (hand region) is selected by eliminating noise through morphological operation and determining a threshold value proportional to the image size. This study focuses on the noise reduction and can be used as a base technology of gesture recognition application.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.595-604
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• 2013

Currently, registered number of imported vehicles is increasing rapidly over the years. Accordingly, environment improvements of vehicle maintenance company for maintenance of luxury vehicle such as imported vehicle are continuously being made. In this paper, we propose a key frame extraction method based on HSV color model for smart vehicle management system implementation to offer for customer reliability of maintenance vehicle. After automatically recognize the license plates of the vehicle using vehicle license plate recognition system when the vehicle come in the car center, we check the repair history and request of the vehicle based on it. We implement mobile services which provide extracted key frame images to the user after extract key frames from vehicle repair video. In addition, we verify the superiority of key frame extraction method by applying a smart vehicle management system. Finally, we convert the RGB color to HSV color to improve the performance of proposed key frame extraction scheme. As a result, we confirmed that our scheme is more excellence about 30% in terms of recall than RGB color model from the performance evaluations.