• Korean Journal of Bronchoesophagology
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• v.18 no.2
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• pp.45-48
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• 2012

Background Neoplastic vessels tend to proliferate on the surface of malignant lesions in the aerodigestive tract. So, superficial malignant lesions can be detected earlier by enhancing mucosal vascular clarity. To enhance mucosal vascular clarity on endoscopic image, we developed an image processing algorithm of RGB (red-green-blue) channel substitution image (CSI). Methods Each pixel in original white light image (WLI) has its own value of red, green and blue channel. Various combinations of RGB channel substitution was tried on original WLI. Results To make superficial blood vessels darker than brighter background mucosa, in the CSI algorithm, RGB value in each pixel of WLI is substituted; red value to green one, green value to blue one. There was a good contrast between superficial mucosal vessels and background brighter mucosa in the CSI image. Conclusion By RGB CSI algorithm, WLI could be successfully converted to new images with enhanced mucosal vascular clarity. Using RGB CSI algorithm could provide added vascular visibility on original WLI.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.95-114
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• 2022

This study presented the possibility of automatic frost observation and the related image data acquisition through the design and installation of a Multiple-sensor based Frost Observation System (MFOS). The MFOS is composed of an RGB camera, a thermal camera and a leaf wetness sensor, and each device performs complementary roles. Through the test operation of the equipment before the occurrence of frost, the voltage value of the leaf wetness sensor increased when maintaining high relative humidity in the case of no precipitation. In the case of Gapyeong- gun, the high relative humidity was maintained due to the surrounding agricultural waterways, so the voltage value increased significantly. In the RGB camera image, leaf wetness sensor and the surface were not observed before sunrise and after sunset, but were observed for the rest of the time. In the case of precipitation, the voltage value of the leaf wetness sensor rapidly increased during the precipitation period and decreased after the precipitation was terminated. In the RGB camera image, the leaf wetness sensor and surface were observed regardless of the precipitation phenomenon, but the thermal camera image was taken due to the precipitation phenomenon, but the leaf wetness sensor and surface were not observed. Through, where actual frost occurred, it was confirmed that the voltage value of leaf wetness sensor was higher than the range corresponding to frost, but frost was observed on the surface and equipment surface by the RGB camera.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7182-7188
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• 2015

Recently, to improve manual mapping method, digital mapping using high-resolution camera and laser scanner has increasingly attracted attention and the relevant study is reportedly on the rise. In this study, laboratory and field test which are intended to quantitatively analyze and estimate the weathering degree which is one of the engineering characteristics of tunnel face using laser scanner that integrates high-solution imaging technique have been conducted. In a bid to analyze correlation between reflectance of laser scanner and weathering degree, investigation of the factors influencing on reflectance was carried out. And to minimize the effect on RGB value which is one the critical influential factors on reflectance, modification equation obtained from lab test was proposed. Modification equation proposed in this study was verified by comparing the values before and after correction using the data obtained by scanning. Consequently, weathering index estimated by modified reflectance appeared very similar with dominant weathering degree and weathering ratio monitored at the field.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.35-42
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• 2014

It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.

• Journal of the Korean Academy of Clinical Electrophysiology
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• v.8 no.2
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• pp.39-43
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• 2010

Purpose : The purpose of this study was to analyze skin reactions induced by ultraviolet irradiation using digital imagery. Methods : We recruited 15 women and ultraviolet irradiation was applied to their lumbar area. (The degree of inflammatory reaction was set on the basis of the third erythema dose. Image analysis was divided by Photoshop CS (8 bit RGB scale and gray scale). Then, images were processes using Image Pro Plus 4.5 program analyzing R, G, B, chromatic red value, luminance value and gray value. Results : As a result of analyzing changes in RGB scale, there were statistically significant differences in R, G, and chromatic red values. As a result of analyzing changes in gray scale, there were statistically significant differences in gray value. Analysis of changes in B and luminance values showed that there was no statistically significant difference. Conclusion : This study found that ultraviolet irradiation had influence on RGB and gray scale. These results suggest that changes to digital images on skin reaction by ultraviolet irradiation are related to erythema. In particular, these changes are related to R and gray values.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.345-347
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• 1998

스캐너를 가지고 이미지를 스캔하면 RGB 값을 얻는다. 이 RGB 값은 스캐너의 빛을 인지하는 소자들의 하드웨어적인 특성이 더해진 장치 의존적인 값이다. 그래서 RGB 값은 왜곡된 칼라 정보를 가지고 있다. 그러므로 칼라 보정을 하기 위해서는 장치 독립적이 값으로 변환해야 한다. 본 논문에서는 장치 독립적인 값을 구하기 위해서 칼라 샘플들을 XYZ로 계측한 값과 400nm에서 700nm 사이의 파장을 계측한 분광 반사값(Spectral reflectance value)을 가지고 스캐너의 칼라 보정을 구현하였다. 구현 방법으로는 신경회로망의 오차 역전파(Error Back Propagation) 알고리즘을 사용하였고 두 가지의 데이터를 가지고 실험했을 때의 결과와 장단점을 비교하였다.