• Journal of the Korean Society for Railway
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• v.6 no.1
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• pp.66-72
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• 2003

Railway sign(graphic sign, diagraphics) designs require good recognition with universality to transmit accurate and speedy information as they connect people around the station and other transport systems. An important point of signs is how to design and deliver the contents to the viewers as a communication service tools. It needs to establish design guidelines with standardization and unified system to show their contents and images clearly like common language with visuality, attractivity and generality. These requisites are important for both aesthetics legibility and unified standards to maximize the effectiveness of pictograms for the use of the general public, who require systematic suggestion and management.

• Journal of Integrative Natural Science
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• v.3 no.1
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• pp.38-42
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• 2010

인간은 정보전달을 위하여 언어 이외에 동작, 표정과 같은 비언어적인 수단을 이용한다. 이러한 비언어적인 수단을 정확히 분석 할 수 있다면 인간과 컴퓨터간의 자연스럽고 지적인 인터페이스를 구축할 수 있게 된다. 본 논문은 별도의 센서를 부착하지 않은 단일 카메라 환경에서 손 형상을 입력정보로 사용하여 손 영역만을 분할한 후 자기 조직화 특징 지도(SOFM: Self Organized Feature Map) 신경망 알고리즘을 이용하여 손 형상을 인식함으로서 수화인식을 위한 보다 안정적이며 강인한 인식 시스템을 구현하고자 한다. 제안 방법으로는 피부색 정보를 이용하여 배경으로부터 손 영역만을 추출한 후 추출된 손 영역의 형상을 인식한다(전처리과정으로 모델이미지의 사이즈와 압축 및 컬러에 대한 정보를 정규화 시켰다). 또한 인식 효율을 높이기 위해 SOFM 신경망 알고리즘을 적용함으로서 보다 안정적으로 손 형상을 인식할 수 있게 되었으며, 손 형상 인식률에 대한 안전성과 정확성을 향상시킬 수 있었다. 그리고 인식된 손 형상의 의미를 텍스트로 보여줌으로서 사용자의 의사를 정확하게 전달할 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.85-91
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• 1995

This paper presents a system which recognizes the Korean Sign Language(KSL) and translates into normal Korean speech. A sign language is a method of communication for the deaf-mute who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the same form of a gesture produced by two signers with their hands may not produce the same numerical values when obtained through electronic sensors. In this paper, we propose a dynamic gesture recognition method based on feature analysis for efficient classification of hand motions, and on a fuzzy min-max neural network for on-line pattern recognition.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.45-50
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• 2009

This paper presents an active vision system for on-line traffic sign recognition. The system is composed of two cameras, one is equipped with a wide-angle lens and the other with a telephoto lends, and a PC with an image processing board. The system first detects candidates for traffic signs in the wide-angle image using color, intensity, and shape information. For each candidate, the telephoto-camera is directed to its predicted position to capture the candidate in a large size in the image. The recognition algorithm is designed by intensively using built in functions of an off-the-shelf image processing board to realize both easy implementation and fast recognition. The results of on-road experiments show the feasibility of the system.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.312-322
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• 2012

The paper presents an approach for recognizing the broken area of the traffic signs. The method is based on the Recognition System for Traffic Signs (RSTS). This paper describes an approach to using the location histogram matching for the broken traffic signs recognition, after the general process of the image detection and image categorization. The recognition proceeds by using the SIFT matching to adjust the acquired image to a standard position, then the histogram bin will be compared preprocessed image with reference image, and finally output the location and percents value of the broken area. And between the processing, some preprocessing like the blurring is added in the paper to improve the performance. And after the reorganization, the program can operate with the GPS for traffic signs maintenance. Experimental results verified that our scheme have a relatively high recognition rate and a good performance in general situation.

• ETRI Journal
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• v.45 no.6
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• pp.1022-1034
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• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1106-1110
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• 2004

A human sign language is generally composed of both static and dynamic gestures. Each gesture is represented by a hand shape, its position, and hand movement (for a dynamic gesture). One of the problems found in automated sign language translation is on segmenting a hand movement that is part of a transitional movement from one hand gesture to another. This transitional gesture conveys no meaning, but serves as a connecting period between two consecutive gestures. Based on the observation that many dynamic gestures as appeared in Thai sign language dictionary are of quasi-periodic nature, a method was developed to differentiate between a (meaningful) dynamic gesture and a transitional movement. However, there are some meaningful dynamic gestures that are of non-periodic nature. Those gestures cannot be distinguished from a transitional movement by using the signal quasi-periodicity. This paper proposes a hybrid method using a combination of the periodicity-based gesture segmentation method with a HMM-based gesture classifier. The HMM classifier is used here to detect dynamic signs of non-periodic nature. Combined with the periodic-based gesture segmentation method, this hybrid scheme can be used to identify segments of a transitional movement. In addition, due to the use of quasi-periodic nature of many dynamic sign gestures, dimensionality of the HMM part of the proposed method is significantly reduced, resulting in computational saving as compared with a standard HMM-based method. Through experiment with real measurement, the proposed method's recognition performance is reported.

• Journal of Korea Multimedia Society
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• v.4 no.3
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• pp.238-246
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• 2001

We have developed the sign-language synthesis system, which can be applied for intelligent terminal equipments, for the purpose of communications between normal people and the hearing-impaired. In the system, we generate the behavior of the sign-language words using CG animation based on Motion-Primitives of the motion observed of each legion of the body in the generation of words, the conventional system was difficult to control the shape of hands and the motions of hands and shoulder, requiring lots of time for the processing. Also it is a big problem to make a large database of sign-language, because it requires over 5,000 words to translate the sign-language. Therefore, in this paper, we propose the new system that is easy to construct the database by using Motion-Primitives, which can make paths of various motions more smooth than conventional systems. We have tested 100 words of the sign-language against the hearing-impaired with the proposed system. As the result of testing by the proposed system, we have earned a good recognition rate with 82%. On the other hand, we had earned the recognition ratio with 76% by using the former system.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.50-59
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• 2014

This paper proposes a method of localization of vehicle especially the horizontal position for the purpose of recognizing the driving lane. Through tracking road signs, the relative position between the vehicle and the sign is calculated and the absolute position is obtained using the known information from the regulation for installation. The proposed method uses Kalman filter for road sign tracking and analyzes the motion using the pinhole camera model. In order to classify the road sign, ORB(Oriented fast and Rotated BRIEF) features from the input image and DB are matched. From the absolute position of the vehicle, the driving lane is recognized. The Experiments are performed on videos from the highway driving and the results shows that the proposed method is able to compensate the common GPS localization errors.

• 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.