• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1890-1900
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• 2017

Tangram game consists of seven pieces of polygons such as triangle, square, and parallelogram. Typical methods of image processing for object recognition may suffer from the existence of side thickness and shadow of the puzzle pieces that are dependent on the pose of 3D-shaped puzzle pieces and the direction of light sources. In this paper, we propose an image processing method that recognizes simple convex polygon-shaped objects irrespective of thickness and pose of puzzle objects. Our key algorithm to remove the thick side of piece of puzzle objects is based on morphological operations followed by logical operations with edge image and background image. By using the proposed object recognition method, we are able to implement a stable tangram game applications designed for tablet computers with front camera. As the experimental results, recognition rate is about 86 percent and recognition time is about 1ms on average. It shows the proposed algorithm is fast and accurate to recognize tangram blocks.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.338-342
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• 2007

In this paper we propose a user adaptive post-processing method to improve the accuracy of speaker dependent, isolated word speech recognition, particularly for mobile devices. Our method considers the recognition result of the basic recognizer simply as a high-level speech feature and processes it further for correct recognition result. Our method learns correlation between the output of the basic recognizer and the correct final results and uses it to correct the erroneous output of the basic recognizer. A multi-layer perceptron model is built for each incorrectly recognized word with high frequency. As the result of experiments, we achieved a significant improvement of 41% in recognition accuracy (41% error correction rate).

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.884-891
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• 2008

As the scale of RFID systems grows, decrease in recognition rate of collision between readers is becoming an issue. Time division technique may solve the problem, but this will cause total recognition time to increase. This issue must be solved for real-time inventory control. In this study, total recognition time was improved by placing ARCM in RFID systems. ARCM maintains short recognition time through efficient utilization of frequency channel and manages RFID systems to recognize the tag by avoiding interference. As the result of using ARCM, saving maximum 20% of time was possible.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.60-68
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• 2014

Extensive researches on PC control system via interaction between PC and users are being conducted recently, especially on human finger recognition in the electronic devices. Heretofore, researches on methods to improve the accuracy of finger recognition in a single device and to control the devices with it have been the mainstream, whereas many different industries where finger recognition become more utilized are demanding researches on methods to selectively control the system of multiple devices for applications in various environments and situations. This article demonstrates attempts to selectively control one of two devices through finger recognition. Along with this, experiments conducted with 6 variable conditions are demonstrated here, where the optimal condition to increase the rate of successful selective finger recognition between two devices is studied.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1393-1402
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• 2013

Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.150-155
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• 2004

In this paper, an emotion recognition method using speech signal is presented. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. The proposed recognizer have each codebook constructed by using the wavelet transform for the emotional state. Here, we first verify the emotional state at each filterbank and then the final recognition is obtained from a multi-decision method scheme. The database consists of 360 emotional utterances from twenty person who talk a sentence three times for six emotional states. The proposed method showed more 5% improvement of the recognition rate than previous works.

• Convergence Security Journal
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• v.14 no.7
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• pp.23-28
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• 2014

This study proposes the design and implementation of the system for a facial expression recognition system. LDP(Local Directional Pattern) feature computes the edge response in a different direction from a pixel with the relationship of neighbor pixels. It is necessary to be estimated that LDP code can represent facial features correctly under various conditions. In this respect, we build the system of facial expression recognition to test LDP performance quickly and the proposed evaluation system consists of six components. we experiment the recognition rate with local micro patterns (LDP, Gabor, LBP) in the proposed evaluation system.

• Journal of Technologic Dentistry
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• v.15 no.1
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• pp.43-61
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• 1993

This study was conducted to determine factors influencing dental technicians recognition level of their occupational disease. After self-administered questionnaire were distributed by mail to 540 technicians clustered samplely semplely selected from dental laboratories resistered in seoul and pusan Korean Dental Laboratory Association 395 technicians responded from march 29 through April 27, 1993. The results are as follows. 1. The recognition level of an occupational disease of the total 395 respondents by sex is higher among male than female. The difference was found to be meaningful(p <.05). 2. When the recognition level of an occupational disease being tested with 45 as the highest point possible, the average point 31.41 $\pm$ 6.50 of the total respondents reflected a high level of recognition. The highly recognized items were stress, bronchial disease, hearing loss. 3. With the highest points in Wallston and Wallstons' health locus of control in personality being 54, the average points of the dental technicians in the study was 35.41 $\pm$ 4.93. 4. As for the medical care patterns, the rate was higher among local medical insurance 64.4% than none 16.8%, company medical isurance 9.2%, medical aide 6.7%, others 2.6%. As for the experience of utilization of outpatient servelies, Yes was 40.4% and 59.6%, showing a meaningful difference(t=.80, p<.05).01) accounted total variance of the factors influencing dental technicians recognition level of their occupational disease(p<.0.000), R-squaire is 0.08.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.257-264
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• 2005

The number of vehicles are rapidly increased as our society is developed. The vehicle recognition has been studied for a while because many people acknowledged it has critical functions to solve the problems of traffic control or vehicle-related crimes. In this paper a novel method is proposed to recognize vehicle models corresponding makers. Vehicles' models are recognized based on the texture parameters from segmented radiator region above a number plate. A three-layer neural network was built and trained with the texture features for recognition. The proposed method shows $93.7\%$ of recognition rate and $99.7\%$ of specificity for vehicles' model.

• The Korean Journal of Health Service Management
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• v.6 no.2
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• pp.121-131
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• 2012

This study investigated the difference of the university students' recognition, attitude, and knowledge level of health by the health education in Daejeon and Chungnam province. First, in the survey analysis result regarding the health related recognition, the students who had attended lectures of health education were surveyed not only to have higher health related recognition than the students who did not have attended the lecture of health education, but also to appear meaningful in statistics by showing significance level p<0.05 in total score. Second, in the research regarding the health related attitude, the attendance or nonattendance of health education lecture were surveyed not to have high effect on the attitude of daily lives of the university students. Third, in the analysis of health related knowledge, the knowledge level between the students who had attended lectures of health education and who did not have attended the lecture of health education showed that the students who had attended lectures of health education got more excellent correct answer rate. In this survey result, the students who had attended lectures of health education showed higher health related recognition and knowledge than the students who had not attended lectures of health education.