• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.7
• /
• pp.1040-1046
• /
• 2015

This paper introduces GP(Genetic Programming) based color detection model for an object detection of humanoid robot vision. Existing color detection methods have used linear/nonlinear transformation of RGB color-model. However, most of cases have difficulties to classify colors satisfactory because of interference of among color channels and susceptibility for illumination variation. Especially, they are outstanding in degraded images from robot vision. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various environments in robot vision for real humanoid Nao.

• Journal of Digital Contents Society
• /
• v.18 no.8
• /
• pp.1641-1648
• /
• 2017

The main purpose of this paper is to compare the performances of various filters with color images to remove the noise. Furthermore, we suggest a modified de-noising process by the transformation of color model from RGB to another color models, such as HSV and $YC_BC_R$, to improve the quality of de-noising methods encompassing Median, Wiener, and Mean filters. Neither the performance comparison of the de-noising filters with color images nor the converting the color model for better de-noise on the degraded images haven't been performed before. Inspired to make improvements, we conduct experiments with new de-noising process on color images. The result of the experiments is shown that it could assist on certain filters being more reliable techniques.

• The Journal of the Korea Contents Association
• /
• v.9 no.4
• /
• pp.1-11
• /
• 2009

To classifying and filtering of adult images, in recent the computer vision techniques are actively investigated because rapidly increase for the amount of adult images accessible on the Internet. In this paper, we investigate and develop the tool filtering of adult images using skin color model. The tool is consisting of two steps. In the first step, we use a skin color classifier to extract skin color regions from an image. In the nest step, we use a region feature classifier to determine whether an image is an adult image or not an adult image depending on extracted skin color regions. Using histogram color model, a skin color classifier is trained for RGB color values of adult images and not adult images. Using SVM, a region feature classifier is trained for skin color ratio on 29 regions of adult images. Experimental results show that suggested classifier achieve a detection rate of 92.80% with 6.73% false positives.

• The Journal of the Korea Contents Association
• /
• v.18 no.10
• /
• pp.431-442
• /
• 2018

2-dimensional barcode, QR code has been used for containing various information such as image, video, map, and business cards. Currently, a smartphone is used as a QR code scanner, displaying the code and converting it to a standard URL for a website. However, QR codes are not very common in encrypted application and so have a few applications. This paper proposes a new color-code, which integrates the conventional QR code and color design, and can be effectively used in some product certification system. The proposed method exploits the fact that genuine code is produced by CMYK color model, but the counterfeit is captured by RGB color model and during this process, color information of the code is changed. This paper introduces the color matrix model to measure the distortion between genuine code and counterfeit code. By investigating the statistical characteristics of color matrix, an effective detection of print image forgeries are designed. Various experiments with color codes show that the proposed system can be effectively used in product certification systems.

• The Bulletin of The Korean Astronomical Society
• /
• v.38 no.2
• /
• pp.79.2-79.2
• /
• 2013

The distributions of Horizontal Branch (HB) star color, temperature, and mass encode a great deal of information on the stellar evolutionary and (possibly) dynamical processes taking place in Globular Clusters (GCs). An accurate physical modeling of the Red Giant Branch (RGB) mass-loss process is key to solving the so-called second parameter problem. In my poster I will present the most recent advancements of an analytical model for mass-loss along the RGB. The model predicts the HB mass distribution with remarkable accuracy over a sample of 4 GCs. These results were submitted as a paper to ApJ (Pasquato et al. 2013, ApJ submitted), but here I expand on them presenting refinements to the model and a comparison with HB masses obtained from Galex ultraviolet observations.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.1 s.33
• /
• pp.67-72
• /
• 2005

Most of image binarization algorithms analyzes the intensity distribution using the histogram for the determination of threshold value. When the intensity difference between the foreground object and the background is great, the histogram shows the tendency to be bimodal and the selection of the histogram valley as the threshold value shows the good result. On the other side. when the intensity difference is not great and the histogram doesn't show the bimodal property, the histogram analysis doesn't support the selection of the proper threshold value. This Paper Proposed the novel binarization method that applies the fuzzy membership function to each color value on the RGB color model and, by using the operation results, separates the features having the great readability from the background. The proposed method prevents the loss of information incurred by the gray scale conversion by using the RGB color model and extracts effectively the readable features by using the fuzzy inference Compared with the traditional binarization methods, the proposed method is able to remove the majority of noise areas and show the improved results on the image of transport containers , etc.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.4
• /
• pp.15-22
• /
• 2003

A film scanner is one of the input devices for ac acquiring high resolution and high qualify of digital images from the existing optical film. Recently the demand of film scanners have risen for experts of image printing and editing fields. However, due to the nonlinear characteristic of light source and sensor, colors of the original film image do not correspond to the colors of the scanned image. Therefore color correction for the scanned digital image is essential in film scanner. In this paper, neural network method is applied for the color correction to CIE L/sup *//a/sup *//b/sup */ color model data converted from RGB color model data. Also a film scanner hardware with 12 bit color resolution for each R, G, B and 2400 dpi is implemented by using the TMS320C32 DSP chip and high resolution line sensor. An experimental result shows that the average color correction rate is 79.8%, which is an improvement of 43.5% than our previous method, polygonal regression method.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.1
• /
• pp.43-50
• /
• 2003

Today, the demand of image acquisition systems grows as the multimedia applications go on increasing greatly. Among the systems, film scanner is one of the systems, which can acquire high quality and high resolution images. However due to the nonlinear characteristic of the light source and sensor, colors of the original film image do not correspond to the colors of the scanned image. Therefore color correction mr the scanned digital image is essential in the film scanner. In this paper, polynomial regression method is applied for the color correction to CIE $L^{*}$ $a^{*}$ $b^{*}$ color model data converted from RGB color model data. A1so a film scanner hardware with 12 bit color resolution for each R, G, B and 2400 dpi was implemented by using TMS320C32 DSP chip and high resolution line sensor. An experimental result shows that the average color difference ($\Delta$ $E^{*}$$_{ab}$ ) is reduced from13.48 to 8.46.6.6.6.6.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.2
• /
• pp.208-217
• /
• 2001

Model-based color image halftoning method using dot-pattern database is proposed for low-resolution color image printing. Dot-pattern database used in the proposed method is based on Blue-Noise Mask. The database consists of dot-patterns constructed by circular dot-overlap model according to each color value. In halftoning procedure, input color value is reproduced as the dot-pattern selected to minimize the difference between the color values of the original image and those of the printed image. Also, the contrast sensitivity function as a human visual model is used to improve the perceived quality of the printed image in dot-pattern selection. Thus, the proposed method can substantially reproduce the color values of the pixels in original image and obtain better image quality. In the experiment, the proposed method has less ΔΕ/Sub ab/ between the original image in monitor and the printed one than that of ED and BNM halftoning. This result approves that the proposed method reproduces better image quality.

• Journal of Korea Multimedia Society
• /
• v.24 no.9
• /
• pp.1224-1241
• /
• 2021

Recent development of deep learning techniques for image generation has led to straightforward generation of sophisticated deepfakes. However, as a result, privacy violations through deepfakes has also became increased. To solve this issue, a number of techniques for deepfake detection have been proposed, which are mainly focused on RGB channel-based analysis. Although existing studies have suggested the effectiveness of other color model-based analysis (i.e., Grayscale), their effectiveness has not been quantitatively validated yet. Thus, in this paper, we compare the effectiveness of Grayscale channel-based analysis with RGB channel-based analysis in deepfake detection. Based on the selected CNN-based models and deepfake datasets, we measured the performance of each color model-based analysis in terms of accuracy and time. The evaluation results confirmed that Grayscale channel-based analysis performs better than RGB-channel analysis in several cases.