• Journal of Korean Academy of Oral Health
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• v.41 no.2
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• pp.154-161
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• 2017

Objectives: The purpose of this study was to investigate changes in public recognition of parabens on Twitter and the research status of parabens related to toothpaste. Methods: Tweet information between 2010 and October 2016 was collected by an automatic web crawler and examined according to tweet frequency, key words (2012-October 2016), and issue tweet detection analyses to reveal changes in public recognition of parabens on Twitter. To investigate the research status of parabens related to toothpaste, queries such as "paraben," "paraben and toxicity," "paraben and (toothpastes or dentifrices)," and "paraben and (toothpastes or dentifrices) and toxicity" were used. Results: The number of tweets concerning parabens sharply increased when parabens in toothpaste emerged as a social issue (October 2014), and decreased from 2015 onward. However, toothpaste and its related terms were continuously included in the core key words extracted from tweets from 2015. They were not included in key words before 2014, indicating that the emergence of parabens in toothpaste as a social issue plays an important role in public recognition of parabens in toothpaste. The issue tweet analysis also confirmed the change in public recognition of parabens in toothpaste. Despite the expansion of public recognition of parabens in toothpaste, there are only seven research articles on the topic in PubMed. Conclusions: The general public clearly recognized parabens in toothpaste after emergence of parabens in toothpaste as a social issue. Nevertheless, the scientific information on parabens in toothpaste is very limited, suggesting that the efforts of dental scientists are required to expand scientific knowledge related to parabens in oral hygiene measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.34-37
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• 2007

Face recognition is still a challenging problem in pattern recognition field which is affected by different factors such as facial expression, illumination, pose etc. The facial feature such as eyes, nose, and mouth constitute a complete face. Mouth feature of face is under the undesirable effect of facial expression as many factors contribute the low performance. We proposed a new approach for face recognition under facial expression applying two cascaded classifiers to improve recognition rate. All facial expression images are treated by general purpose classifier at first stage. All rejected images (applying threshold) are used for adaptation using GA for improvement in recognition rate. We apply Gabor Wavelet as a general classifier and Gabor wavelet with Genetic Algorithm for adaptation under expression variance to solve this issue. We have designed, implemented and demonstrated our proposed approach addressing this issue. FERET face image dataset have been chosen for training and testing and we have achieved a very good success.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.3-55
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• 2001

The implementation of human-like robot has been advanced in various parts such as mechanic arms, legs, and applications of five senses. The vision applications have been developed in several decades and especially the face recognition have become a prominent issue. In addition, the development of computer systems makes it possible to process complex algorithms in realtime. The most of human recognition systems adopt the discerning method using fingerprint, iris, and etc. These methods restrict the motion of the person to be discriminated. Recently, the researchers of human recognition systems are interested in facial recognition by using machine vision. Thus, the object of this paper is the implementation of the realtime ...

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.326-341
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• 2020

Based on theoretical background of Situational Theory of Problem Solving(STOPS), we have examined the role of STOPS variables on a group of online public in their issue recognition and communicative behavior on Korea's nationwide civil boycott movement of Japanese goods. Results from 524 survey cases from a Korea's major online community show that two independent variables(Problem Recognition, Referent Criterion) revealed positive in their effect on mediating variable(Situational Motivation in Problem Solving). Situational Motivation also revealed positive in its effect on six dependent variables(Information Forefending, Information Permitting, Information Forwarding, Information Sharing, Information Seeking, Information Attending) of Communicative Behavior. Involvement Recognition and Constraint Recognition revealed positive without proper statistical significance. As a result, study on the case of online public in Korea supports STOPS theory as high-level of Problem Recognition and Referent Criterion effects on Communicative Behavior in positive way via Situational Motivation. Implications from the findings have discussed and proposed suggestions for government public relations and further studies.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.528-531
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• 2003

Recognition of licence plate becomes a key issue to many traffic related application such as road traffic monitoring or parking lots access control. In this paper, the brightness, YIQ and HSI methods were used to locate a license. After the characters in license plate were extracted, template matching method was applied for character recognitions. To test the performance of the proposed algorithm, images of seventy vehicle were tested. The success rates for license plate and character recognition were approximately 99%, and 96%, respectively

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.960-967
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• 2013

Shape recognition of solder ball bumps in a BGA (Ball Grid Array) is an important issue in flip chip bonding technology. In particular, the semiconductor industry has required faster and more accurate inspection of micron-size solder bumps in flip chip bonding as the density of balls has increased dramatically. The difficulty of this issue comes from specular reflection on the metal ball. Shape recognition of a metal ball is a very realproblem for computer vision systems. Specular reflection of the metal ball appears, disappears, or changes its image abruptly due to tiny movementson behalf of the viewer. This paper presents a practical shape recognition method for three dimensional (3-D) inspection of a BGA using a 5-step ring illumination device. When the ring light illuminates the balls, distinctive specularity images of the balls, which are referred to as "iso-slope contours" in this paper, are shown. By using a mathematical reflectance model, we can drive the 3-D shape information of the ball in aquantitative manner. The experimental results show the usefulness of the method for industrial application in terms of time and accuracy.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.