• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.331-335
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• 2001

Cl\iAC model was proposed by Albus [6J to formulate the processing characteristics of the human cerebellum. Instead of the global weight updating scheme used in the back propagation, CMAC use the local weight updating scheme. Therefore, CMAC have the advantage of fast learning and high convergence rate. In this paper, simulate Color Calibration by CMAC in color images and design hardware by VHDL-base high-level synthesis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1477-1481
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• 2010

In this paper, we propose the classification and tracking method of the hand region using deformable template and condensation. To do this, first, we extract the hand region by using the fuzzy color filter and HCbCr color model. Second, we extract the edge of hand by applying the Canny edge algorithm. Third, we find the first template by calculating the conditional probability between the extracted edge and the model edge. If the accurate template of the first object is decided, the condensation algorithm tries to track it. Finally, we demonstrate the effectiveness and feasibility of the proposed method through some experiments.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.289-297
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• 2007

Color image scale captures the knowledge of colorists and represents both adjectives and colors in the same adjective image scales in order to select color(s) corresponding to an adjective. Due to the difficulty of psychological experiment and statistical analysis, in general, only a limited number of colors are located in the color image scales. This can make color selection process hard especially to non-expert. In this paper, we propose an interpolation of color image scale based on the fuzzy K-nearest neighbor method, which provides continuous colors according to the coordinates of the image scales. The experimental results show that the interpolated image scales can be practically useful for color selection process.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.19-23
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• 2013

TIn this paper, we propose a novel fuzzy stretching method that adopts a triangle type fuzzy membership function to control the highest and lowest brightness limit dynamically. As an essential procedure to enhance the brightness contrast, stretching is an important procedure in color image processing. While popular Ends-in Search stretching method should be provided fixed minimum and maximum brightness threshold from experience, our proposed method determines them dynamically by fuzzy membership functions. The minimum and maximum limit is determined by computing the lowest and highest pixel value according to the membership degree of our designed triangle type membership function. The experiment shows that the proposed method result in far less skewed histogram than those of Ends-in Search stretching thus successfully verifies its effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.80-87
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• 2004

In this paper we proposed the novel feature extraction method that is able to extract the individual characters from the license plate area of the car image more precisely by using the RGB color information and the fuzzy binarization newly proposed. The proposed method, first, extracts from the original image the areas that the pixels with the colors around the green are concentrated on as the candidate areas of the license plate, and selects the area with the most intensive distribution of pixels with the white color among the candidate areas as the license plate area. Second the noises of the license plate area should be removed by using 34{\times}$3 Sobel masking, and the fuzzy binarization method are proposed and applied to the license plate area to generate the binarized image of the license plate area. Lastly, the application of the contour tracking algorithm to the binarized area extracts the individual characters from the license plate area. The experiment on a variety of the real car images showed that the proposed method generates the higher rate of success for character extraction than the previous methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.106-110
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• 2014

This paper proposed the face recognition method which can be effectively applied to the robot's vision system. The proposed algorithm is recognition using hue extraction and feature point. hue extraction was using difference of skin color, pupil color, lips color. Features information were extraction from eye, nose and mouth using feature parameters of the difference between the feature point, distance ratio, angle, area. Feature parameters fuzzified data with the data generated by membership function, then evaluate the degree of similarity was the face recognition. The result of experiment are conducted with frontal color images of face as input images the received recognition rate of 96%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.237-240
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• 2006

Human's sight holds the most extents among other senses. It will become more beneficial in person's emotion or body, if we form much better environment to human in connection with visual information as importance of visual information. Human is using a lot of display units on modern society. Basic colors that compose these are Red, Green and Blue. Using these three colors, we can change color sense of monitor or brightness degree. Suitable color degree by individual's environment can reduce person's stress or give comfortable feeling. So Factors by human's emotion and environment are standardized using fuzzy and the method that is to apply the result of Intelligent Color Control(ICC) on display is proposed.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.214-220
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• 2021

In this study, we constructed several scenarios that are required for LED lighting, and we designed and implemented an LED lighting control system to operate these scenarios to confirm their behavior. An LED lighting control system is a hybrid control board that is designed by combining LED controllers and SMPS, consisting of an AC/DC power supply part that converts AC 220 V into DC 12 V, and a drive and control part that controls the scenario and color of the LED module. Conventional LED light controllers have an input power of DC 12 V, so when using the input AC 220 V, the SMPS must be connected to the LED light controller. To eliminate this inconvenience, a hybrid LED lighting control system was configured to combine LED lighting controllers and SMPS into one control system. Furthermore, we designed a control system to represent the most appropriate color according to the input of the distance and illumination using a fuzzy control system to conduct computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.526-537
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• 1999

In the paper. we propose an evaluation model based the adaptive fuzzy systems, which can transform the physical features of a color pattern to the emotional features. The model is motivated by the Soen's psychological experiments, in which he found the physical features such as average hue, saturation, intensity and the dynamic components of the color patterns affects to the emotional features represented by a pair of adjective words having the opposite meanings. Our proposed model consists of two adaptive fuzzy rule-bases and the y-model, a l i r ~ r ys et operator, to fuze the evaluation values produced by them. The model shows con~parablep erformances to the neural network for the approximation of the nonlinear transforms, and it has the advantage to obtain the linbwistic interpretation from the trained results. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1731-1741
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• 1999

Color images are often corrupted by the noise due to noisy sensors or channel transmission errors. Some filters such as vector media and vector $\alpha$-trimmed mean filter have bee used for color noise removal. In this paper, We propose the fuzzy cluster filters based on the possibilistic c-means clustering, because the possibilistic c-means clustering can get robust memberships in noisy environments. Also, we propose weighted vector $\alpha$-trimmed mean filter to improve the conventional vector $\alpha$-trimmed mean filter. In this filter, the central data are more weighted than the outlying data. In this paper, we implemented the color noise generator to evaluate the performance of the proposed filters in the color noise environments. The NCD measure and visual measure by human observer are used for evaluation the performance of the proposed filters. In the experiment, proposed fuzzy cluster filters in the sense of NCD measure gave the best performance over conventional filters in the mixed noise. Simulation results showed that proposed weighted vector $\alpha$-trimmed mean filters better than the conventional vector $\alpha$-trimmed mean filter in any kinds of noise.