• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.696-704
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• 2013

In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.

• Journal of Satellite, Information and Communications
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• v.8 no.3
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• pp.52-57
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• 2013

This paper describes the color comparison analysis of flame in each standard color model in order to propose the optimal color model for image processing based flame detection algorithm. Histogram intersection values were used to analyze the separation characteristics between color of flame and color of non-flame in each standard color model which are RGB, YCbCr, CIE Lab, HSV. Histogram intersection value in each color model and components is evaluated for objective comparison. The analyzed result shows that YCbCr color model is the most suitable for flame detection by average HI value of 0.0575. Among the 12 components of standard color models, each Cb, R, Cr component has respectively HI value of 0.0433, 0.0526, 0.0567 and they have shown the best flame separation characteristics.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.513-520
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• 2020

Recently, the development of a vision inspection system using machine learning has become more active. This study seeks to develop a defect inspection model using machine learning. Defect detection problems for images correspond to classification problems, which are the method of supervised learning in machine learning. In this study, defect detection models are developed based on algorithms that automatically extract features and algorithms that do not extract features. One-dimensional CNN and two-dimensional CNN are used as algorithms for automatic extraction of features, and MLP and SVM are used as algorithms for non-extracting features. A defect detection model is developed based on four models and their accuracy and AUC compare based on AUC. Although image classification is common in the development of models using CNN, high accuracy and AUC is achieved when developing SVM models by converting pixels from images into RGB values in this study.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1835-1838
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• 2003

Texts in natural images contain significant and detailed informations about the images. Thus, to extract those texts correctly, we suggest a text extraction method using color variance feature. Generally, the texts in images have color variations with the backgrounds. Thus, if we express those variations in 3 dimensional RGB color space, we can emphasize the text regions that can be hard to be captured with a method using intensity variations in the gray-level images. We can even make robust extraction results with the images contaminated by light variations. The color variations are measured by color variance in this paper. First, horizontal and vertical variance images are obtained independently, and we can fine that the text regions have high values of the variances in both directions. Then, the two images are logically ANDed to remove the non-text components with only one directional high variance. We have applied the proposed method to the multiple kinds of the natural images, and we confirmed that the proposed feature can help to find the text regions that can he missed with the following features - intensity variations in the gray-level images and/or color continuity in the color images.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.159-166
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• 2013

In this paper, we propose a transmission rate compensation method to remove a haze of an image by using edge information of a haze image and image segmentation. With a hazed image, it is difficult not only to recognize objects in the image but also to use an image processing method. One of the famous defogging algorithm named 'Dark Channel Prior'(DCP) is used to predict fog transmission rate using dark area of an image, and eliminates fog from the image. But there is a big possibility to calculate a wrong transmission rate if the area of high RGB values is larger than the area of the reference area. Therefore we eliminate color distortion area to calculate transmission rate by using the propose method, and obtain a natural clean image from a hazed image.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.255-262
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• 2007

This paper proposes an enhanced skin color-based detection method to find a region of human face in color images. The proposed detection method combines three color spaces, RGB, $YC_bC_r$, YIQ and builds color union histograms of luminance and chrominance components respectively. Combined color union histograms are then fed in to the back-propagation neural network for training and Levenberg-Marquadt algorithm is applied to the iteration process of training. Proposed method with Levenberg-Marquadt algorithm applied to training process of neural network contributes to solve a local minimum problem of back-propagation neural network, one of common methods of training for face detection, and lead to make lower a detection error rate. Further, proposed color-based detection method using combined color union histograms which give emphasis to chrominance components divided from luminance components inputs more confident values at the neural network and shows higher detection accuracy in comparison to the histogram of single color space. The experiments show that these approaches perform a good capability for face region detection, and these are robust to illumination conditions.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.15-24
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• 2011

Recently various anatomic researches by using serially sectioned images in VK(Visible Korean) project are in progress. In that sense, it is very important and necessary to represent images which keep information of the original anatomic images. In this regard, there are some studies to get rid of slice patterns in coronal and sagittal images. However, according to the rapid spatial frequency changes, the ringing effect occurred in common. In addition, because of merge with the original images which have splice patterns, those patterns appeared again. Therefore, in this study we found effective color space to apply to FFT(Fast Fourier Transform) and notch filter in getting rid of the slice patterns. To verify this, we used RGB, LAB, CMYK, HSV and HSL color space. Secondly we got rid of and alleviated the ringing effect by improving notch filter. To verify this, we compared proposed method with previous study on the basis of original images by visual method and objective values. These result images are expected to contribute to anatomy research in relation to VK project.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1397-1403
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• 2018

Today, modern people perceive skin care as part of their physical health care, and acne is a common skin disease problem that is found on the face. In this paper, an effective acne detection algorithm using CIE $L^*a^*b^*$ color space has been proposed. It is red when the pixel value of the component image $a^*$ is a positive number, so it is suitable for detecting acne in skin image. First, the skin image based on the RGB color space is subjected to light compensation through color balancing, and converted into a CIE $L^*a^*b^*$ color space. The extracted component image $a^*$ was normalized, and then the skin and acne area were estimated with the threshold values. Experimental results show that the proposed method detects acne more effectively than the conventional method based on brightness information, and the proposed method is robust against the reflected light source.

• The Journal of the Korea Contents Association
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• v.6 no.7
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• pp.90-98
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• 2006

Digital watermarking is a technique embedding hidden information into multimedia data imperceptibly such as images and sounds. Generally an original image is transformed and coded watermark data is embedded in frequency domain watermarking models. In this paper, We propose a new color image watermarking technique using Fresnel transform. A watermark image is Fresnel - transformed and the intensity of transformed pattern is embedded into color image. In our watermarking model, an original image is converted from RGB components into YCrCb components and then the values of real number and imaginary number of a Fresnel-transformed pattern of a watermark image are embedded into Y component. The watermarking experiments were conducted to show the validity of the proposed method using PSNR value, and the results show that our method has the robustness against lossy compression like JPEG.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.2
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• pp.11-19
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• 2003

This paper proposes an algorithm to reduce the estimation error of surface spectral-reflectance(SR) using a conventional 3-band RGB camera. In the proposed method, estimation error can be reduced by using adaptive principal components(PCs) for each color region. In order to build adaptive set of PCs, n SR populations are organized for n PC sets by using Lloyd quantizer design algorithm. Macbetch ColorCheckcer is utilized as initial representative SR values for 1485 Munsell color chips of total color population and the Munsell chips arc divided subsets and a set of corresponding adaptive PCs per each subset is organized. As a result of experiments, the proposed method showed advanced estimation performance compared to both the two 3-band PCA methods and the 5-band wiener method.