• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.1
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• pp.121-133
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• 2007

The CMS(color management system) software was to enable consistent color reproduction from original to reproduction. The CMS was to create RGB monitor and printer characterization profiles and then use the profiles for device independent color transformation. The implemented CMM(color management module) used the CIELAB color space for the profile connection. Various monitor characterization model was evaluated for proper color transformation. To construct output device profile, SLI(sequential linear interpolation) method was used for the color conversion from CMYK device color to device independent CIELAB color space and tetrahedral interpolation method was used for backward transformation. UCR(under color removal) based black generation algorithm was used to construct CIELAB to CMYK LUT(lookup table). When transforming the CIE Lab colour space to CMYK, it was possible to involve the gray revision method regularized in the brightness into colour transformation process and optimize the colour transformation by black generation method based on UCR technique. For soft copy colour proofing, evaluating several monitor specialism methods showed that LUT algorithm was useful. And it was possible to simplify colour gamut mapping by constructing both the look-up table and the colour gamut mapping algorithm to a reference table.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.101-107
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• 2012

This study describes a proposed method of converting an input sound signal into a color image by emulating human synesthetic skills which make it possible to associate an sound source with a specific color image. As a first step of sound-to-image conversion, features such as fundamental frequency(F0) and energy are extracted from an input sound source. Then, a musical scale and an octave can be calculated from F0 signals, so that scale, energy and octave can be converted into three elements of HSI model such hue, saturation and intensity, respectively. Finally, a color image with the BMP file format is created as an output of the process of the HSI-to-RGB conversion. We built a basic system on the basis of the proposed method using a standard C-programming. The simulation results revealed that output color images with the BMP file format created from input sound sources have diverse hues corresponding to the change of the F0 signals, where the hue elements have different intensities depending on octaves with the minimum frequency of 20Hz. Furthermore, output images also have various levels of chroma(or saturation) which is directly converted from the energy.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.37-43
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• 2014

In this paper, we propose a robust lip detection algorithm using color clustering. At first, we adopt AdaBoost algorithm to extract facial region and convert facial region into Lab color space. Because a and b components in Lab color space are known as that they could well express lip color and its complementary color, we use a and b component as the features for color clustering. The nearest neighbour clustering algorithm is applied to separate the skin region from the facial region and K-Means color clustering is applied to extract lip-candidate region. Then geometric characteristics are used to extract final lip region. The proposed algorithm can detect lip region robustly which has been shown by experimental results.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.557-564
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• 2000

This paper presents a watermarking algorithm for embedding visually recognizable pattern (Mark, Logo, Symbol, stamping or signature) into the image. first, the color image(RGB model)is transformed in YCbCr model and then the Y component is transformed into 3-level wavelet transform. Next, the values are assembled with pattern watermark. PN(pseudo noise) code at spread spectrum communication method and mutilevel watermark weights. This values are inserted into discrete wavelet domain. In our scheme, new calculating method is designed to calculate wavelet transform with integer value in considering the quantization error. and we used the color conversion with fixed-point arithmetic to be easy to make the hardware hereafter. Also, we made the new solution using mutilevel threshold to robust to common signal distortions and malicious attack, and to enhance quality of image in considering the human visual system. the experimental results showed that the proposed watermarking algorithm was superior to other similar water marking algorithm. We showed what it was robust to common signal processing and geometric transform such as brightness. contrast, filtering. scaling. JPEG lossy compression and geometric deformation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.4-36
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• 2001

A robust and reliable path recognition system is one necessary component for the autonomous navigation of a mobile robot to help determining its current position in its navigation map. This paper describes a computer visual path-recognition system using on-board video camera as vision-based driving assistance for an autonomous navigation mobile robot. The common problem for a visual system is that its reliability was often influenced by different lighting conditions. Here, two different image processing methods for the path detection were developed to reduce the effect of the luminance: one is based on the RGB color model and features of the path, another is based on the HSV color model in the absence of luminance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.35-46
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• 2007

This paper proposes a six-color separation method of reducing unnecessary usage of colorants based on the limitation of total colorant amount and dot visibility ordering. First, the CIELAB values of input RGB image are estimated through the color-mixing model and compared with pre-calculated CIELAB values corresponding to all combination of CMYKlclm colorants with a constraint of color difference, thereby selecting initial CMYKlclm candidates. Next, the limitation on total colorant amount Is imposed on initial CMYKlclm candidates to remove the excessive amounts of colorants, and then final CMYKlclm candidates are determined by minimizing the usage of light cyan and light magenta in the dark region based on the dot visibility ordering of C, M, Y, K, lc, and lm. Through the experiment, the proposed method is shown to reduce the excessive amount of colorants with preserving good image quality.

• Journal of Internet of Things and Convergence
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• v.10 no.6
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• pp.15-21
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• 2024

In response to the recent government's AI and semiconductor talent training policy, this study proposes a method of effectively classifying semiconductor chips and detecting defects in RGBA color space using AI deep learning technology. Quality assurance and defect detection of semiconductor chips are essential to ensure the reliability and performance of electronic devices. However, traditional inspection methods mainly include visual inspection, mechanical measurement, and electrical testing, which are time-consuming, expensive for state-of-the-art equipment, and inefficient for many production environments due to inspection. To solve this problem, image analysis techniques based on deep learning are attracting attention in automated inspection systems. Through this experiment, it was confirmed that the deep learning model using RGBA color space shows excellent performance in defect detection and classification of semiconductor chips. In particular, RGBA color space including alpha channel provides more accurate and precise results for defect detection than conventional RGB color space models with less learning. The results of this experiment suggest that the RGBA color space can play an important role in the deep learning-based defect detection system, and further experiments in various datasets and conditions will expand the scope of the method's use in the future. Such a model is highly likely to contribute to the automation and quality improvement of the semiconductor manufacturing process. This study aims to improve the accuracy and efficiency of the semiconductor chip inspection process by utilizing the advantages of RGBA color space.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.586-594
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• 2018

In this paper, we propose a livestock theft detection system through moving object classification and tracking method. To do this, first, we extract moving objects using GMM(Gaussian Mixture Model) and RGB background modeling method. Second, it utilizes a morphology technique to remove shadows and noise, and recognizes moving objects through labeling. Third, the recognized moving objects are classified into human and livestock using skeletal features and color similarity judgment. Fourth, for the classified moving objects, CAM (Continuously Adaptive Meanshift) Shift and Kalman Filter are used to perform tracking and overlapping judgment, and risk is judged to generate a notification. Finally, several experiments demonstrate the feasibility and applicability of the proposed method.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.2
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• pp.16-22
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• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.