• 한국멀티미디어학회논문지
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• 제21권3호
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• pp.348-357
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• 2018

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.

• 한국IT서비스학회지
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• 제6권2호
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• pp.1-18
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• 2007

Many researches have been done in insurance fraud as the amount and frequency of insurance fraud have been increasing continuously. In particular, the development of insurance fraud detection system using large database management techniques including data mining or link analysis based on visual method have been the main research topic in insurance fraud. However, this kinds of detection system were very ineffective to find unintentional insurance fraud happened by accident even though it was so good to find intentional and organized crime insurance fraud. Therefore, this research suggests insurance fraud as an ethical decision making and applies TPB(Theory of Planned Behavior) for the finding of reasons and prevention strategies of unintentional insurance fraud happened by accident. The results of research show that TPB is very appropriate model to explain the behavior of insurance fraud and that insurance agents force to do insurance fraud as affecting perceived behavior control. Therefore, education and pubic relations for insurance fraud are very effective for preventing insurance fraud and developing insurance service industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.345-359
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• 2017

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.385-399
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• 2017

Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.

• 대한원격탐사학회지
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• 제33권1호
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• pp.89-95
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• 2017

Ship detection in synthetic aperture radar(SAR)imagery has long been an active research topic and has many applications. In this paper,we propose an efficient method for detecting ships from SAR imagery using filtering. This method exploits ship masking using a median filter that considers maximum ship sizes and detects ships from the reference image, to which a Non-Local means (NL-means) filter is applied for speckle de-noising and a differential image created from the difference between the reference image and the median filtered image. As the pixels of the ship in the SAR imagery have sufficiently higher values than the surrounding sea, the ship detection process is composed primarily of filtering based on this characteristic. The performance test for this method is validated using KOMPSAT-5 (Korea Multi-Purpose Satellite-5) SAR imagery. According to the accuracy assessment, the overall accuracy of the region that does not include land is 76.79%, and user accuracy is 71.31%. It is demonstrated that the proposed detection method is suitable to detect ships in SAR imagery and enables us to detect ships more easily and efficiently.

• 로봇학회논문지
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• 제13권1호
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• pp.31-37
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• 2018

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

• 제어로봇시스템학회논문지
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• 제21권11호
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• pp.1017-1026
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• 2015

Infrared (IR) target detection is one of the key technologies in Automatic Target Detection/Recognition (ATD/R) for military applications. However, IR sensors have limitations due to the weather sensitivity and atmospheric effects. In recent years, sensor information fusion study is an active research topic to overcome these limitations. SAR sensor is adopted to sensor fusion, because SAR is robust to various weather conditions. In this paper, a Boolean Map Visual Theory-Morphology (BMVT-M) method is proposed to detect targets in SAR and IR images. Moreover, we suggest the IR and SAR image registration and decision level fusion algorithm. The experimental results using OKTAL-SE synthetic images validate the feasibility of sensor fusion-based target detection.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.277-301
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• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• 지능정보연구
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• 제22권1호
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• pp.187-204
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• 2016

소셜 미디어를 이용하는 사용자들이 직접 작성한 의견 혹은 리뷰를 이용하여 상호간의 교류 및 정보를 공유하게 되었다. 이를 통해 고객리뷰를 이용하는 오피니언마이닝, 웹마이닝 및 감성분석 등 다양한 연구분야에서의 연구가 진행되기 시작하였다. 특히, 감성분석은 어떠한 토픽(주제)를 기준으로 직접적으로 글을 작성한 사람들의 태도, 입장 및 감성을 알아내는데 목적을 두고 있다. 고객의 의견을 내포하고 있는 정보 혹은 데이터는 감성분석을 위한 핵심 데이터가 되기 때문에 토픽을 통한 고객들의 의견을 분석하는데 효율적이며, 기업에서는 소비자들의 니즈에 맞는 마케팅 혹은 투자자들의 시장동향에 따른 많은 투자가 이루어지고 있다. 본 연구에서는 중국의 온라인 시나 주식 포럼에서 사용자들이 직접 작성한 포스팅(글)을 이용하여 기존에 제시된 토픽들로부터 핫토픽을 선정하고 탐지하고자 한다. 기존에 사용된 감성 사전을 활용하여 토픽들에 대한 감성값과 극성을 분류하고, 군집분석을 통해 핫토픽을 선정하였다. 핫토픽을 선정하기 위해 k-means 알고리즘을 이용하였으며, 추가로 인공지능기법인 SOM을 적용하여 핫토픽 선정하는 절차를 제시하였다. 또한, 로짓, 의사결정나무, SVM 등의 데이터마이닝 기법을 이용하여 핫토픽 사전 탐지를 하는 감성분석을 위한 모형을 개발하여 관심지수를 통해 선정된 핫토픽과 탐지된 핫토픽을 비교하였다. 본 연구를 통해 핫토픽에 대한 정보 제공함으로써 최신 동향에 대한 흐름을 알 수 있게 되고, 주식 포럼에 대한 핫토픽은 주식 시장에서의 투자자들에게 유용한 정보를 제공하게 될 뿐만 아니라 소비자들의 니즈를 충족시킬 수 있을 것이라 기대된다.

• 정보과학회 논문지
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• 제42권12호
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• pp.1522-1534
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• 2015

본 연구에서는 미리 구축해 놓은 학습데이터 없이도 입력된 작문이 주어진 작문 주제에 적합한 내용인지 아닌지를 자동으로 판단할 수 있는 방법을 제안한다. ConceptNet은 다양한 종류의 문서에서 추출한 자연언어 문장들로부터 구축된 그래프 형태의 지식베이스이다. 본 연구에서는 작문 주제에 해당하는 작문 프롬프트(essay prompt)와 ConceptNet만을 이용하여 문서의 주제-이탈 여부를 판별하는 방법을 제안한다. ConceptNet에서 두 개념간의 최단 경로를 찾고 이에 대한 의미 유사도를 계산하는 방법을 제안한다. 이를 이용하여 작문 프롬프트와 수험생 작문 내용을 ConceptNet의 개념들로 매핑하고 이 개념들 사이의 의미 유사도를 계산하여 작문 프롬프트와 수험생 작문 사이의 주제 부합 여부를 판단한다. 8개의 작문 시험을 수행하여 얻은 수험생 작문 데이터에 대하여 평가를 수행한 결과 기존의 연구에 비해 좋은 성능을 얻을 수 있었다. ConceptNet을 활용하면 유의미한 단순 추론이 가능하기 때문에 본 연구에서 제안한 방법은 추론을 요하는 작문 문제에도 적용 가능함을 보였다.