• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.65-70
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• 2000

In general, the quality of agricultural products is determined by direct measurement of a weight or a magnitude, and it is determined by indirect or non-destructive method. In this paper, using color image processing, the algorithm to determine its quality and grading is presented. And the algorithm is applied to real-time citrus classifier. In the system, the size and color of orange are measured by not the sight of human but the digital image processing. The citrus classification system has the real-time maximum classification capacity of six quantify per one second. The system can be applied to controller design for the quality classification of agricultural products.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.295-311
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• 2019

The smart product market is growing year by year and is being used in many areas. There are various ways of interacting with smart products and users by inputting voice recognition, touch and finger movements. It is most important to detect an accurate hand region as a whole step to recognize hand movement. In this paper, we propose a method to detect accurate hand region in real time in various environments. A conventional method of detecting a hand region includes a method using depth information of a multi-sensor camera, a method of detecting a hand through machine learning, and a method of detecting a hand region using a color model. Among these methods, a method using a multi-sensor camera or a method using a machine learning requires a large amount of calculation and a high-performance PC is essential. Many computations are not suitable for embedded systems, and high-end PCs increase or decrease the price of smart products. The algorithm proposed in this paper detects the hand region using the color model, corrects the problems of the existing hand detection algorithm, and detects the accurate hand region based on various experimental environments.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• ETRI Journal
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• v.30 no.1
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• pp.101-112
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• 2008

In this paper, we present a method to customize the ground color in outdoor sports video to provide TV viewers with a better viewing experience or subjective satisfaction. This issue, related to content personalization, is becoming critical with the advent of mobile TV and interactive TV. In outdoor sports video, such as soccer video, it is sometimes observed that the ground color is not satisfactory to viewers. In this work, the proposed algorithm is focused on customizing the ground color to deliver a better viewing experience for viewers. The algorithm comprises three modules: ground detection, shot classification, and ground color customization. We customize the ground color by considering the difference between ground colors from both input video and the target ground patch. Experimental results show that the proposed scheme offers useful tools to provide a more comfortable viewing experience and that it is amenable to real-time performance, even in a software-based implementation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.567-570
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• 2003

A color restoration algorithm for dynamic images under multiple luminance conditions is proposed by using correction vectors, defined for sub regions that the original target is divided into and calculated from color information given in well-illuminated regions. These vectors restore chromatic information of the restored image obtained by the color restoration algorithm in a low luminance condition. Under the condition that the size of dynamic color images in multiple luminance conditions is $320\times240$, experimental results show that the restored image by the proposed algorithm decreases the color-difference about 30％ than that of the restoration algorithm with color change vectors in a low luminance condition. The proposed algorithm aims to construct the surveillance system with a low cost CCD camera in the real world.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.345-352
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• 2014

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert' operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.47-55
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• 2000

Two experiments were conducted to examine the relationships between the color structure and the user preference of a CNS (Car Navigation System) digital map in terms of HSE (Human Sensibility Ergonomics). In the first experiment, the user's preference of color structures were investigated from the subjects' self-designed digital maps using a CNS digital map UIMS (User Interface Management System): in the second, statistical relation models between the user's color structure satisfaction level and the color components of CIE (Commission Internationale de ι'Eclairage) of the real products were suggested. For each experiment, CIE L*u*v* and CIE LCH color space were adapted, respectively, because they have their own characteristics of perceptual uniformity which enables the color components to transform a linear function.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.117-120
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• 2005

The proposed method is based on extracting information from 3-D Fourier spectra calculated from some projection incoherent images. Three colored computer-generated holograms (CGHs) are synthesized from 3-D Fourier spectra. Optically reconstructed full-color images are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.135-135
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks that have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, we have a global search for entire image and then have tracking the object through local search when the object is recognized.