• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.809-818
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• 2007

In this thesis, the color information is used to extract the traffic sign territory, and for recognizing the extracted image, it proposes the traffic sign recognition system that applies the error back propagation algorithm. The proposed method analyzes the color of traffic sign to extract and recognize the possible territory of traffic sign. The method of extracting the possible territory is to use the characteristics of YUV, YIQ, and CMYK color space from the RGB color space. Morphology uses the geometric characteristics of traffic sign to make the image segmentation. The recognition of traffic signs can be recognized by using the error back propagation algorithm. As a result of the experiment, the proposed system has proven its outstanding capability in extraction and recognition of candidate territory without the influence of differences in lighting and input image in various sizes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.333-336
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• 1996

The sign language is a method of communication for deaf person. For sign communication, sign language and manual alphabet are used continuously. In this paper is proposed a system which recognize Korean sign language(KSL) and Korean manual alphabet(KMA) continuously. For recognizing KSL and KMA, basic elements for sign language, namely, the 14 hand directions, 23 hand postures, and 14 hand orientations are used. At first, this system recognize current motion state using speed and change of speed in motion by state automata. Using state, basic element classifiers using Fuzzy Min-Max Neural Network and Fuzzy Rule are executed. Meaning of signed gesture is selected by using basic elements which was recognized.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1662-1665
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• 2002

This paper proposes a novel semantic feature detection (SFD) method for real-time image transmission of sign language and finger spelling. We extract semantic information as an interlingua from input text by natural language processing, and then transmit the semantic feature detection, which actually is a parameterized action representation, to the 3-D articulated humanoid models prepared in each client in remote locations. Once the SFD is received, the virtual human will be animated by the synthesized SFD. The experimental results based on Japanese sign langauge and Chinese sign langauge demonstrate that this algorithm is effective in real-time image delivery of sign language and finger spelling.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.115-122
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• 2017

This paper proposes a deep learning algorithm based sign detection and recognition system for the blind. The proposed system is composed of sign detection stage and sign recognition stage. In the sign detection stage, aggregated channel features are extracted and AdaBoost classifier is applied to detect regions of interest of the sign. In the sign recognition stage, convolutional neural network is applied to recognize the regions of interest of the sign. In this paper, the AdaBoost classifier is designed to decrease the number of undetected signs, and deep learning algorithm is used to increase recognition accuracy and which leads to removing false positives which occur in the sign detection stage. Based on our experiments, proposed method efficiently decreases the number of false positives compared with other methods.

• Journal of Drive and Control
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• v.20 no.4
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• pp.92-99
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• 2023

Autonomous vehicles should recognize and respond to the specified speed to drive in compliance with regulations. To recognize the specified speed, the most representative method is to read the numbers of the signs by recognizing the speed signs in the front camera image. This study proposes a method that utilizes YOLO-Labeling-Labeling-EfficientNet. The sign box is first recognized with YOLO, and the numeric digit is extracted according to the pixel value from the recognized box through two labeling stages. After that, the number of each digit is recognized using EfficientNet (CNN) learned with the virtual environment dataset produced directly. In addition, we estimated the depth of information from the height value of the recognized sign through regression analysis. We verified the proposed algorithm using the virtual racing environment and GTSRB, and proved its real-time performance and efficient recognition performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.100-109
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• 2011

The study concluded that in order to achieve a single sign-on through the federation ID management method must be used among many other methods so to establish a combined approval service that gives access to education related information provided by the 16 city and provincial offices of education and the KERIS(Korea Education & Research Information Service). The federation ID management method allows the usage of authentication policy and enables transfer of ID information to other institutions through standardized processing. Therefore by establishing single sign-on through the federation ID management method allows individual city and provincial office of education to maintain the already existing approval services while combining user identification database.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.93-103
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• 2018

This paper presents a method for speed limit sign recognition in images. Conventional sign recognition methods decreases recognition accuracy because they are very sensitive and include repeated features. The proposed method emphasizes color attributes based on the weighted YUV color space. Moreover, the recognition accuracy can be improved by extracting the local region of interest (ROI) in the candidates. The proposed method uses the Haar features and the Adaboost classifier for recognition. Experimental results confirm that the proposed algorithm is superior to conventional algorithms under various speed signs and conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.57-60
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• 2003

We have proposed the adaptive noise reduction algorithm using the MDFT. The algorithm proposed use the linear prediction coefficients of the AR method based on Sign algorithm that is the modified LMS instead of the least mean square(LMS). The signals with a random noise tracking performance are examined through computer simulations and confirmed that the high speed adaptive noise reduction processing system is realized with rapid convergence.

• Korean Journal of Mathematics
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• v.16 no.2
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• pp.243-257
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• 2008

We seek the sign changing periodic solutions of the nonlinear wave equation $u_{tt}-u_{xx}=a(x,t)g(u)$ under Dirichlet boundary and periodic conditions. We show that the problem has at least one solution or two solutions whether $\frac{1}{2}g(u)u-G(u)$ is bounded or not.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.655-658
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• 1999

In this paper we design a key-frame editor for 3D sign-language animation using the inverse kinematics. Using the editor, we can calculate the joint angles for two arms automatically. Up to now we have computed the values of the joint angles using the forward kinematics, where we have determined the values heuristically based on our experiences. To overcome the drawbacks, we employ the arm transformation matrix of the inverse kinematics. Experimental results show a possibility that the proposed method could be used for making up the sign-language communication dictionaries.