• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.443-449
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• 2015

In this paper, we proposed Channel allocation scheme according to the user's location with IR. In VLC System, LED can generate various colors of light by controlling the mixing ratio of each individual RGB color element. Thus, each RGB channel will have a different signal power, and each channel will have different performance. This proposed system using Visible light(RGB) as way to transmit signals, it depends on the mixture RGB, which decided the color of light, moreover, each things determined their performance. However, if the signal were fixed allocated RGB to transmit such as the original system, the importance of the each signals a different occur the limit on the quality of signals. To solve this problem in this paper, according to the RGB mixture ratios analyze the performance for the LED, which analyzed based on allocating the signal by transmitting to improve the quality was about how researched. In addition, our proposed system is able to improve the performance of BER and satisfied the Qos to desire users.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1945-1953
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• 2022

Korea Aerospace Research Institute produces mosaic images of the Korean Peninsula every year to promote the use of satellite images and provides them to users in the public sector. However, since the pan-sharpening and color balancing methodologies are applied during the mosaic image processing, the original spectral information is distorted. In addition, there is a limit to analyze using mosaic images as mosaic images provide only Red, Green and Blue bands excluding Near Infrared (NIR) band. Therefore, in order to compensate for these limitations, this study applied the Principal Component Analysis (PCA) technique and indices extracted from R, G, B bands together for image classification and compared the classification results. As a result of the analysis, the accuracy of the mosaic image classification result was about 67.51%, while the accuracy of the image classification result using both PCA and RGB indices was about 75.86%, confirming that the accuracy of the image classification result can be improved. As a result of comparing the PCA and the RGB indices, the accuracy of the image classification result was about 64.10% and 74.05% respectively. Through this, it was confirmed that the classification accuracy using the RGB indices was higher among the two techniques, and implications were derived that it was important to use high quality reference or supplementary data. In the future, additional indices and techniques are needed to improve the classification and analysis results of mosaic images, and related research is expected to increase the utilization of images that provide only R, G, B or limited spectral information.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.76-76
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• 2013

The current hierarchical model of galaxy formation predicts that galaxy halos contain merger relics in the form of long stellar streams. In order to find stellar substructures in galaxy, we focused our investigation on the stellar spatial density around globular clusters and on the quantitative properties of the evolved sequences in the color-magnitude diagrams (CMDs). First, we investigated the spatial configuration of stars around five metal-poor globular clusters in halo region (M15, M30, M53, NGC 5053, and NGC 5466) and one metal-poor globular cluster in bulge region (NGC 6626). Our findings indicate that all of these globular clusters show strong evidence of extratidal features in the form of extended tidal tails around the clusters. The orientations of the extratidal features show the signatures of tidal tails tracing the clusters' orbits and the effects of dynamical interactions with the galaxy. These features were also confirmed by the radial surface density profiles and azimuthal number density profiles. Our results suggest that these six globular clusters are potentially associated with the satellite galaxies merged into the Milky Way. Second, we derived the morphological parameters of the red giant branch (RGB) from the near-infrared CMDs of 12 metal-poor globular clusters in the Galactic bulge. The photometric RGB shape indices such as colors at fixed magnitudes, magnitudes at fixed colors, and the RGB slope were measured for each cluster. The magnitudes of the RGB bump and tip were also estimated. The derived RGB parameters were used to examine the overall behavior of the RGB morphology as a function of cluster metallicity. The behavior of the RGB shape parameters was also compared with the previous observational calibration relation and theoretical predictions of the Yonsei-Yale isochrones. Our results of studies for stellar spatial distribution around globular clusters and the morphological properties of RGB stars in globular clusters could add further observational evidence of merging scenario of galaxy formation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1319-1324
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• 2012

Tomatoes were harvested at different ripening stages. To determine the ripening stages, We analyzed the relation between the compound concentrations of tomato measured with HPLC and the tomato RGB images. Among the compound concentrations, tomato quality is mostly affected by the Lycopene. The $Q^2$ error of the predicted Lycopene concentration and the corresponding independent component of tomato RGB image, determined from the PLS procedure, was 0.92. and we show the effectiveness of the independent component by comparing the error between the pixel area of RGB image applied by independent component and the simple black white tomato image. This regression made it possible to construct concentration images of the tomatoes, which showed non-uniform ripening. The method can be applied in an unsupervised real time sorting machine of unripe and discolored tomato using the compound concentrations.

• Smart Media Journal
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• v.9 no.3
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• pp.46-51
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• 2020

Pine wilt disease is one of the reasons that results in huge damage on pine trees in east Asia including Korea, Japan, and China, and early finding and removing the diseased trees is an efficient way to prevent the forest from wide spreading. This paper proposes a searching method of the damaged pine trees from wilt disease in ortho-images corrected from RGB images, which are captured by unmanned aviation vehicles. The proposed method constructs patch-based classifier using ResNet18 backbone network, classifies the RGB ortho-image patches, and make the results as a heat map. The heat map can be used to find the distribution of diseased pine trees, to show the trend of spreading disease, and to extract the RGB distribution of the diseased areas in the image. The classifier in the work shows 94.7% of accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1681-1688
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• 2017

In this paper, we implemented a high-speed signal processing hardware system for Color Line Scan Camera using FPGA and Nor-Flash. The existing hardware system mainly processed by high-speed DSP based on software and it was a method of detecting defects mainly by RGB individual logic, however we suggested defect detection hardware using RGB-HSL hardware converter, FIFO, HSL Full-Color Defect Decoder and Image Frame Buffer. The defect detection hardware is composed of hardware look-up table in converting RGB to HSL and 4K HSL Full-Color Defect Decoder with high resolution. In addition, we included an image frame for comprehensive image processing based on two dimensional image by line data accumulation instead of local image processing based on line data. As a result, we can apply the implemented system to the grain sorting machine for the sorting of peanuts effectively.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.541-550
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• 1994

A true color image is needed many data on the occasion of the transmission and storage. Therefore, we want to describe color image by a minority data without unreasonableness at eyesight. In this paper, it is presented 256 colormap construction method in RGB, YIQ/YUV space and common colormap expression method at merge between colormaps by reason of dissimilar original color image to display at a monitor for each other colormap at the same time. In comparison with processed result in RGB, YIQ/YUV space, it was measured by PSNR, standard variation, and edge preservation rate using sobel operator. Process time is 3second in colormap construction and 2second in merge between colormaps. In the PSNR value, RGB space has higher 0.15, 0.34 on an average than YIQ and YUV spae. Standard variation has lower in 0.15, 0.41 on an average than Yiq and YUV space. But in the data compression, YIQ/YUV space have about 1/3 compression efficiency than RGB space by reason of use to only 4bit of 8bit in color component.

• 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.