• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.768-775
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• 2005

For sign languages are main communication means among hearing-impaired people, there are communication difficulties between speaking-oriented people and sign-language-oriented people. Automated sign-language recognition may resolve these communication problems. In sign languages, finger spelling is used to spell names and words that are not listed in the dictionary. There have been research activities for gesture and posture recognition using glove-based devices. However, these devices are often expensive, cumbersome, and inadequate for recognizing elaborate finger spelling. Use of colored patches or gloves also cause uneasiness. In this paper, a vision-based finger spelling recognition system is introduced. In our method, captured hand region images were separated from the background using a skin detection algorithm assuming that there are no skin-colored objects in the background. Then, hand postures were recognized using a two-dimensional grid analysis method. Our recognition system is not sensitive to the size or the rotation of the input posture images. By optimizing the weights of the posture features using a genetic algorithm, our system achieved high accuracy that matches other systems using devices or colored gloves. We applied our posture recognition system for detecting Korean Sign Language, achieving better than $93\%$ accuracy.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.167-172
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• 2012

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate has the disadvantage that require sophisticated smoothing process. Gaussian mixtures in order to improve them with a continuous probability density CHMM (Continuous Hidden Markov Model) model is proposed for the optimization of the library system. In this paper is system configuration thread control in recognition Gaussian mixtures model provides a model to optimize of the CHMM vocabulary recognition. The result of applying the proposed system, the recognition rate of 98.1% in vocabulary recognition, respectively.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.10-19
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• 2013

This Paper proposed the development of new magnetic sensor and recognition system to expendite pattern recognition of a handwriting character. Received character graphics should be performed the session and balancing and no extraction of end points, bend points and juntions separately. The Artifical intelligence algorithm is adapted to structure snalysis and recognition process by individual basic letter dictionary except for the handwriing character graphic dictionaryimproving error of recognition algorithm and enomous dictionary for generalization. In this Paper, recognition rate of the received character are compared with pre registered character at letter dictionary for performance test of magnetic ball sensor. As a result of unicode conversion and eomparison, the artificial intelligence study have recognition rate more than 95% at initial recognition rate of 70%.

• 한국ITS학회:학술대회논문집
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• 2005.11a
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• pp.75-78
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• 2005

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.683-687
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• 2010

This paper studied some methods which use frequency warping method that is the one of the speech transformation method to develope the robust speech recognition system for the emotional variation. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions and it is observed that speech spectrum is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, new training method that uses frequency warping in training process is presented to reduce the effect of emotional variation and the speech recognition system based on vocal tract length normalization method is developed to be compared with proposed system. Experimental results from the isolated word recognition using HMM showed that new training method reduced the error rate of the conventional recognition system using speech signal containing various emotions.

• Journal of the Korean Society for Railway
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• v.14 no.2
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• pp.130-136
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• 2011

This paper suggests realization method for real-time speech recognition using VoiceXML in telephony environment based on SIP for Railroad Reservation Service. In this method, voice signal incoming through PSTN or Internet is treated as dialog using VoiceXML and the transferred voice signal is processed by Speech Recognition System, and the output is returned to dialog of VoiceXML which is transferred to users. VASR system is constituted of dialog server which processes dialog, APP server for processing voice signal, and Speech Recognition System to process speech recognition. This realizes transfer method to Speech Recognition System in which voice signal is recorded using Record Tag function of VoiceXML to process voice signal in telephony environment and it is played in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.191-197
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• 2007

Vision system is an object recognition system analyzing image information captured through camera. Vision system can be applied to various fields, and vehicle recognition is ole of them. There have been many proposals about algorithm of vehicle recognition. But have complex calculation processing. So they need long processing time and sometimes they make problems. In this research we suggested vehicle type recognition system using vision bpx based on embedded platform. As a result of testing this system achieves 100% rate of recognition at the optimal condition. But when condition is changed by lighting, noise and angle, rate of recognition is decreased as pattern score is lowered and recognition speed is slowed.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.

• Journal of the Korean Home Economics Association
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• v.41 no.4
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• pp.71-83
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• 2003

The purpose of this research is to analyse the factors affecting household's educational expenditure on on-line educational system, student's recognition of better remarks effect, and satisfaction of on-line educational system. The sample of this study was 484 parents who had experiences of using on-Une educational system for their children being from preschool to high school. The results were as follows ; First, the household's educational expenditure on on-line educational system was affected from the sex, children's school level, and using on-line educational system or not. Second, the significant variables which affected students' recognition of better remarks effect were husbands' job, number of children, the first child's school level, households' monthly expenditure, students' remarks at school, and using on-line educational system or not. Third, the satisfaction of on-line educational system was affected from husband's job, using on-line educational system or not, and the students' recognition of better remarks effect.