• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.1-9
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• 2015

In this paper, we implemented the computer vision platform design with MEAN Stack through Raspberry PI 2 model which is an open source platform. we experimented the face recognition, temperature and humidity sensor data logging with WiFi communication under Raspberry Pi 2 model. Especially we directly made the shape of platform with 3D printing design. In this paper, we used the face recognition algorithm with OpenCV software through haarcascade feature extraction machine learning algorithm, and extended the functionality of wireless communication function ability with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the vision platform for identifying the face recognition characteristics of scanning with PI camera with gathering the temperature and humidity sensor data under IoT environment. and made the vision platform with 3D printing technology. Especially we used MongoDB for developing the performance of vision platform because the MongoDB is more akin to working with objects in a programming language than what we know of as a database. Afterwards, we would enhance the performance of vision platform for clouding functionalities.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.465-473
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• 2017

Electrocardiogram(ECG) signal is one of the unique bio-signals of individuals and is used for personal authentication. The existing studies on personal authentication method using ECG signals show a high detection rate for a small group of candidates, but a low detection rate and increased execution time for a large group of candidates. In this paper, we propose a hierarchical algorithm that extracts fiducial points based on curvature of ECG signals as feature values for grouping candidates ​and identifies candidates using waveform-based comparisons. As a result of experiments on 74 ECG signal records of QT-DB provided by Physionet, the detection rate was about 97% at 3-heartbeat input and about 99% at 5-heartbeat input. The average execution time was 22.4 milliseconds. In conclusion, the proposed method improves the detection rate by the hierarchical personal authentication process, and also shows reduced amount of computation which is plausible in real-time personal authentication usage in the future.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.379-388
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• 2002

In this paper, the real-time algorithm is proposed for automatic detection of the face and facial features. In the face region, we extracted eyes, nose, mouth and so forth. There are two methods to extract them; one is the method of using the location information of them, other is the method of using Gaussian second derivatives filters. This system have high speed and accuracy because the facial feature extraction is processed only by detected face region, not by whole image. There are some kinds of good experimental result for the proposed algorithm; high face detection rate of 95%, high speed of lower than 1sec. the reduction of illumination effect, and the compensation of face tilt.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.497-501
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• 2008

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classily multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.429-431
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• 1998

본 논문에서는 의료 영상 중에서 성대 운동의 불규칙적인 움직임을 판단하여 자동으로 진단 파라미터를 구하는 비디오스트로보키모그래피(Videostrobokymography) 시스템에서 관심 영역을 추출하는 방법을 소개하고자 한다. CCD카메라에 의해 촬영된 영상은 비디오 테이프에 저장된 후 이미지 캡쳐 보드에서 그레이 이미지(gray-level)로 변환되어 저장된다. 입력된 영상은 움직이는 영상을 촬영한 것이므로 관심 영역의 위치가 각 프레임마다 다르다. 또한 실제로 입력된 성대영상들이 점진적인 농도 변화를 보이기 때문에 에지에 의해 영역을 추출하는 일반적인 영역 추출방법은 사용하기 어렵다. 본 논문에서는 두 번의 단계를 통하여 관심 영역을 추출하고 있다. 첫 번째는 입력된 영상에서 노이즈를 제거한 후 각 프레임에서 영상의 최소 에너지를 구한다. 두 번째로 농도 변화 값을 특징 값으로 이용하는 분할-합병 알고리즘(Split-merge Algorithm)을 적용하여 관심 영역을 추출하였다. 제안한 알고리즘을 19명의 성대 영상에 적용하여 분석한 결과 성대의 관심 영역을 추출할 수 있었다. 그리고, 영상의 에너지 값을 이용하는 스네이크 알고리즘(Snake Algorithm)에 적용하여 비교해본 결과 본 연구에서 제안하는 스네이크 알고리즘보다 좋은 성능을 보임을 확인할 수 있었다. 본 연구에서 제안하는 관심 영역 추출 방법은 동적인 변화를 보이는 영상에서 관심 영역을 추출할 수 있을 뿐 아니라 계산 량이 적어 200x280크기의 이미지를 초당 약 40프레임에 대한 관심 영역을 추출할 수 있는 장점이 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.237-241
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• 2011

Stable operation of an industry crane becomes more important as current industry facilities become larger and operate at higher speeds. This paper proposes implementing a system for monitoring moving objects within safety lines of an industry crane by camera. The cost of implementing such a system is low, since it requires only a webcam and notebook computer. The detection algorithm of moving objects uses the feature extraction method by image differential histograms. The proposed system is robust to variations in the weather and environment. The area of the inside safety lines is considered and shadow removal algorithm is used for good performance of the system. The system is valuable for practical applications in the industry.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.26-31
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• 2011

In this paper we show the LCD simulator for defect inspection using image processing algorithm and neural network. The defect inspection algorithm of the LCD consists of preprocessing, feature extraction and defect classification. Preprocess removes noise from LCD image, using morphology operator and neural network is used for the defect classification. Sample images with scratch, pinhole, and spot from real LCD color filter image are used. From some experiments results, the proposed algorithms show that defect detected and classified in the ratio of 92.3% and 94.5 respectively. Accordingly, in this paper, a possibility of practical implementation of the LCD defect inspection system is finally suggested.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.162-167
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• 2005

Recently, the advanced industries using micro parts are rapidly growing. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro to micro parts. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This study deals with condition monitoring using acoustic emission(AE) signal in the micro-grooving. First, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by using the fuzzy C-means algorithm, which is one of the methods to recognize data patterns. These result is effective monitoring method of micro endmill state by the AE sensing techniques which can be expected to be applicable to micro machining processes in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1490-1499
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• 1999

This paper proposes a new handwritten numeral recognition method using numeral shape grasps and FSOM (Fuzzy Self-Organizing Map). The proposed algorithm is based on the idea that numeral input data with similar shapes are classified into the same class. Shapes of numeral data are created using lines of external-contact and the class of numeral data is determined by template matching of the shapes. Each class of numeral data has FSOM and feature extraction method, respectively. In this paper, we divide the numeral database into the 16 classes. The divided FSOM model allows not only an independent learning phase of SOM but also step-by-step learning. Experiments using Concordia University handwritten numeral database proved that the proposed algorithm is effective to improve recognition accuracy.

• Journal of Physiology & Pathology in Korean Medicine
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• v.29 no.3
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• pp.218-222
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• 2015

Facial diagnosis based on quantitative facial features has been studied in many Korean medicine fields, especially in Sasang constitutional medicine. By the rapid growing of 3D measuring technology, generic and cheap 3D sensors, such as Microsoft Kinect, is popular in many research fields. In this study, the possibility of using Kinect in facial diagnosis is examined. We introduce the development of facial feature extraction system and verify its accuracy and repeatability of measurement. Furthermore, we compare Sasang constitution diagnosis results between DSLR-based system and the developed Kinect-based system. A Sasang constitution diagnosis algorithm applied in the experiment was previously developed by a huge database containing 2D facial images acquired by DSLR cameras. Interrater reliability analysis result shows almost perfect agreement (Kappa = 0.818) between the two systems. This means that Kinect can be utilized to the diagnosis algorithm, even though it was originally derived from 2D facial image data. We conclude that Kinect can be successfully applicable to practical facial diagnosis.