• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.999-1002
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• 2003

In this study, embodied supervisory system that apply motion detection technique to small web camera and detects watch picture. Motion detection technique that use pixel value of car image that use in existing need memory to store background image. Also, there is sensitive shortcoming at increase of execution time by data process of pixel unit and noise. Suggested technique that compare extracting motion information by block unit to do to have complexion that solve this shortcoming and is strong at noise. Because motion information by block compares block characteristic value of image without need frame memory, store characteristic cost by block of image. Also, can get effect that reduce influence about noise and is less sensitive to flicker etc.. of camera more than motion detection that use pixel value in process that find characteristic value by block unit.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.299-300
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• 2011

최근 손 동작 인식은 새로운 맨머신인터페이스(man-machine interface)를 위한 기술로 주목 받고 있으며, 이를 위한 손 검출은 손 동작 인식이나 손 추적을 위해 반드시 선행되어야 하는 중요한 기술이다. 기존에 연구되어온 대부분의 손 검출 방법으로는 색상을 기반으로 한 손 검출이었다. 하지만 색상을 기반으로 한 손 검출은 조명의 영향을 많이 받아 신뢰성을 보장하기 어렵다. 이러한 조명의 영향은 깊이 정보(depth information)를 이용함으로써 조명 변화에 강인한 손 검출을 수행할 수 있다. 본 논문에서는 손 검출을 깊이 정보를 활용하여 수행할 수 있는 방법을 제안하였다. 실시간으로 깊이 정보를 생성할 수 있는 depth sensor 하나를 사용하여 깊이 영상을 얻고 노이즈를 개선 해 준 후에 정의된 모션을 사용하여 손의 특징을 추출하여 손 검출을 하였다.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1893-1894
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• 2006

본 논문은 원자로 제어봉구동장치 각 기구의 동작을 감시하여 이 장치에 의한 제어봉의 이동 여부를 검출하는 방법에 관한 것이다. 제어봉구동장치의 일부에서 전기적, 기계적 결함이 생기면 제어봉의 스텝 이동이 제대로 수행되지 못하고, 결국 해당 제어봉은 원자로의 목표 열적 출력을 위한 바른 위치에 도달하지 못하게 된다. 이렇게 되면 제어봉들 사이에 위치 편차가 발생하여 원하는 원자로 열적 출력을 얻을 수 없게 되므로 제어봉구동장치 각 기구의 동작을 신속하고 정확하게 파악하여 해당 제어봉의 이동 여부를 검출할 수 있어야 한다. 제어봉구동장치의 기구들은 각자의 여자 코일의 전류 파형 변화를 통해 동작 여부를 확인할 수 있는데 본 논문에서는 코일 전류 파형을 분석하기 위하여 국소적인 왜곡 검출 능력이 우수한 웨이블릿 변환 기법을 사용하였다.

• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.50-59
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• 2006

This paper proposes an automatic video monitoring system and its application to emergency detection by analyzing human behavior using neural network. The object area is identified by subtracting the statistically constructed background image from the input image. The identified object area then is transformed to the feature vector. Neural network has been adapted for analyzing the human behavior using the feature vector, and is designed to classify the behavior in rather simple numerical calculation. The system proposed in this paper is able to classify the three human behavior: stand, faint, and squat. Experiment results shows that the proposed algorithm is very efficient and useful in detecting the emergency situation.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.940-943
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• 1998

본 논문은 조명의 전체 혹은 국부적인 변화에 견실한 특성을 갖는 동작검출 기법에 관한 것이다. 피사체의 움직임과 조명이 변화의 특성을 파악하기 위해 본 논문에서는 두 영상의 상관계수와 영상의 차이를 종합적으로 이용하여 움직임 여부를 판단할 수 있는 지표를 산정하는 기법을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1280-1286
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• 2001

In this Study, we try to suggest a system that recognize worker's regular motion more effectively First, based on frame difference that separates still background from movable object to video that make a film of worker's motion. The next, with edge detection, estimating the center of motion could recognize continuous motion. By action cognition system that design in this research films worker's action using fixed CCTV to supplement problem of action awareness system that is applied in existent industry spot, various mountings to get action information minimized. Also, shorten session that need in awareness enforcing action awareness through image subtraction and edge detection between frame to reduce time necessary to draw worker's body part special quality, expense designed inexpensive action cognition system as being efficient.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.325-327
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• 2006

본 논문은 모션 그래디언트 히스토그램(Motion Gradient Histogram : 이하 'MGH')을 적용하여 동영상에서 나타나는 다수 객체들의 동작 검출 및 인식을 실시간으로 구현하는 방법을 제안한다. 인식하고자 하는 대상에 대한 기본적인 템플릿 동영상들의 MGH와 일정 프레임 간격마다 동영상의 MGH를 비교하여 검출 및 인식이 이루어진다. 동시에 다수의 동작이 있는 경우 동작이 발생하는 영역을 모션 에너지 영상(Motion Energy Image : MEI) 기법으로 추출하여 해당 영역별 MGH를 구함으로써 다수 동작을 인식할 수 있도록 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.945-952
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• 2013

The R wave of QRS complex is the most prominent feature in ECG because of its specific shape; therefore it is taken as a reference in ECG feature extraction. But R wave detection suffers from the fact that frequency bands of the noise/other components such as P/T waves overlap with that of QRS complex. ECG signal processing must consider efficiency for hardware and software resources available in processing for miniaturization and low power. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, efficient QRS detection based on SOM(Subtractive Operation Method) is presented in this paper. For this purpose, we detected R wave through the preprocessing method using morphological filter, empirical threshold, and subtractive signal. Also, we applied dynamic backward searching method for efficient detection. The performance of R wave detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41% in R wave detection.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.68-73
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• 2013

In this paper, we proposed a readout circuit of pulse waveform and rate for a U-health system to monitor health condition. For long-time operation without replacing or charging a battery, either pulse waveform or pulse rate is selected as the output data of the proposed readout circuit according to health condition of a user. The proposed readout circuit consists of a simple digital logic discriminator and a dual-mode ADC which operates in the ADC mode or in the count mode. Firstly, the readout circuit counts pulse rate for 4 seconds in the count mode using the dual-mode ADC. Health condition is examined after the counted pulse rate is accumulated for 1 minute in the discriminator. If the pulse rate is out of the preset normal range, the dual-mode ADC operates in the ADC mode where pulse waveform is converted into 10-bit digital data with the sampling frequency of 1 kHz. These data are stored in a buffer and transmitted by 620 kbps to an external monitor through a RF transmitter. The data transmission period of the RF transmitter depends on the operation mode. It is generally 1 minute in the normal situation or 1 ms in the emergency situation. The proposed readout circuit was designed with $0.11{\mu}m$ process technology. The chip area is $460{\times}800{\mu}m^2$. According to measurement, the power consumption is $161.8{\mu}W$ in the count mode and $507.3{\mu}W$ in the ADC mode with the operating voltage of 1 V.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.380-392
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• 2013

A method to classify a golf swing motion into 7 sections using a Kinect sensor and a fuzzy system is proposed. The inputs to the fuzzy logic are the positions of golf club and its head, which are extracted from the information of golfer's joint position and color information obtained by a Kinect sensor. The proposed method consists of three modules: one for extracting the joint's information, another for detecting and tracking of a golf club, and the other for classifying golf swing motions. The first module extracts the hand's position among the joint information provided by a Kinect sensor. The second module detects the golf club as well as its head with the Hough line transform based on the hand's coordinate. Using a fuzzy logic as a classification engine reduces recognition errors and, consequently, improves the performance of robust classification. From the experiments of real-time video clips, the proposed method shows the reliability of classification by 85.2%.