• 한국멀티미디어학회논문지
• /
• 제10권12호
• /
• pp.1591-1600
• /
• 2007

Camera position information from 2D face image is very important for that make the virtual 3D face model synchronize to the real face at view point, and it is also very important for any other uses such as: human computer interface (face mouth), automatic camera control etc. We present an algorithm to detect human face region and mouth, based on special color features of face and mouth in $YC_bC_r$ color space. The algorithm constructs a mouth feature image based on $C_b\;and\;C_r$ values, and use pattern method to detect the mouth position. And then we use the geometrical relationship between mouth position information and face side boundary information to determine the camera position. Experimental results demonstrate the validity of the proposed algorithm and the Correct Determination Rate is accredited for applying it into practice.

• 한국군사과학기술학회지
• /
• 제18권5호
• /
• pp.510-516
• /
• 2015

This paper describes an approach to detect interesting objects using satellite images. This paper focuses on the interesting objects that have common special patterns but do not have identical shapes and sizes. The previous technologies are still insufficient for automatic finding of the interesting objects based on operation of special pattern analysis. In order to overcome the circumstances, this paper proposes a methodology to obtain the special patterns of interesting objects considering their common features and their related characteristics. This paper applies MSER(Maximally Stable Extremal Regions) for the region detection and corner detector in order to extract the features of the interesting object. This paper conducts a case study and obtains the experimental results of the case study, which is efficient in reducing processing time and efforts comparing to the previous manual searching.

• Tribology and Lubricants
• /
• 제14권3호
• /
• pp.74-80
• /
• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
• /
• pp.214-216
• /
• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• 전자공학회논문지B
• /
• 제30B권2호
• /
• pp.51-58
• /
• 1993

This paper describes the development of a speaker-dependent isolated word recognizer as applied to voice dialing in a car noise environment. for this purpose, several methods to improve performance under such condition are evaluated using database collected in a small car moving at 100km/h The main features of the recognizer are as follow: The endpoint detection error can be reduced by using the magnitude of the signal which is inverse filtered by the AR model of the background noise, and it can be compensated by using variants of the DTW algorithm. To remove the noise, an autocorrelation subtraction method is used with the constraint that residual energy obtainable by linear predictive analysis should be positive. By using the noise rubust distance measure, distortion of the feature vector is minimized. The speech recognizer is implemented using the Motorola DSP56001(24-bit general purpose digital signal processor). The recognition database is composed of 50 Korean names spoken by 3 male speakers. The recognition error rate of the system is reduced to 4.3% using a single reference pattern for each word and 1.5% using 2 reference patterns for each word.

• 센서학회지
• /
• 제16권5호
• /
• pp.369-376
• /
• 2007

Recently, obesity that is increasingly becoming a major cause of various diseases is emerging as a serious social problem. In order to solve this problem, the necessity of measurement systems for overweight management has increased. This paper is a study on the measurement system for obesity management that can offer right medical services everywhere and allways by analyzing EMG (electromyograph) of the abdomen and then checking one's health state. For analyzing EMG signals of the abdomen, algorithms for energy detection, signal feature extraction, classification and recognition are presented. This paper proposes a system that provides an appropriate an estimation on the health status by evaluating the obesity degree and muscular strength of the abdomen through the system applying these algorithms.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2015년도 하계학술대회
• /
• pp.433-434
• /
• 2015

카메라 캘리브레이션은 다시점 카메라 시스템에서 내부와 외부 인자로 이루어진 카메라 파라미터를 획득하는 과정을 의미 한다. 이는 3차원으로 표현되는 장면과 카메라간의 구조를 다루기 위해 중요하다. 그러나 카메라 캘리브레이션은 사람이 직접 손으로 각 영상에서 사각형의 네 점을 정확히 찍어 주어야 하는 과정 때문에 카메라의 수와 패턴 영상의 수가 늘어남에 따라 상당히 번거로운 작업이 된다. 본 논문에서는 카메라 캘리브레이션 과정에서 손으로 수행하는 작업을 줄이기 위해 자동으로 패턴 특징점을 탐색하는 알고리즘을 제안한다. 제안하는 방법은 먼저 영상에서 패턴 특징점의 후보를 찾기 위해 해리스 코너 검출 방법을 사용한다. 그리고 후보 주변의 원형 샘플 화소를 이용하여 유효한 패턴 특징점을 추출한다. 실험 결과는 Matlab 캘리브레이션 툴박스를 이용하여 획득한 카메라 파라미터와 비교해 보았을 때 큰 차이가 없지만 수작업의 번거로움을 상당히 감소시켰음을 확인하였다.

• Journal of Electrical Engineering and Technology
• /
• 제9권4호
• /
• pp.1375-1384
• /
• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• 한국철도학회논문집
• /
• 제6권2호
• /
• pp.81-86
• /
• 2003

This study proposes the neural network simulator for the integrity evaluation of weld zone in railway rails. For these purposes, the ultrasonic signals for defects(crack) of weld zone in frames are acquired in the type of time series data and echo strength. The detection of the natural defects in railway truck is performed using the characteristics of echodynamic pattern in ultrasonic signal. And then their applications evaluated feature extraction based on the time-frequency-attractor domain(peak to peak, rise time, rise slope, fall time, fall slope, pulse duration, power spectrum, and bandwidth) and attractor characteristics (fractal dimension and attractor quadrant) etc. The constructed neural network simulator agrees fairly well with the measured results of test block(defect location, beam propagation distance, echo strength, etc). The Proposed neural network simulator in this study can be used for the integrity evaluation of weld zone in railway rails.

• 생물환경조절학회지
• /
• 제20권2호
• /
• pp.63-71
• /
• 2011

영상처리는 정확한 오이의 형상 및 위치를 인식하기 위하여 형상인식 알고리즘에 대한 연구를 수행하였다. 다양한 오이형상을 인식하기 위한 방법으로는 신경회로망의 연상 메모리 알고리즘을 이용하여 오이의 특정형상을 인식하였다. 형상인식은 실제영상에서 오이의 형상과 위치를 판정할 수 있도록 알고리즘을 개발한 결과, 다음과 같은 결론을 얻었다. 본 알고리즘에서는 일정한 학습패턴의 수를 2개, 3개, 4개를 각각 기억시켜 샘플패턴 20개를 실험하여 연상시킨 결과, 학습패턴으로 복원된 출력패턴의 비율은 각각 65.0%, 45.0%, 12.5%로 나타났다. 이는 학습패턴의 수가 많을수록 수렴할 때, 다른 출력패턴으로 많이 검출되었다. 오이의 특정형상 검출은 $30{\times}30$간격으로 자동검출 되도록 처리하였다. 실제영상에서 자동 검출로 처리한 결과, 오이인식의 처리시간은 약 0.5~1초/1개(패턴) 빠르게 검출되었다. 또한, 다섯 개의 실제 영상에서 실험한 결과, 학습패턴에 대한 다른 출력패턴은 96~99%의 제거율을 나타내었다. 오이로 인식된 출력패턴 중에서, 오검출된 출력패턴의 비율은 0.1~4.2%를 나타내었다. 본 연구에서는 신경회로망을 이용하여 오이의 형상 및 위치를 인식할 수 있도록 알고리즘을 개발하였다. 오이의 위치측정은 실제영상에서 학습패턴과 유사한 출력패턴의 좌표를 가지고, 오이의 위치좌표를 추정할 수 있었다.