• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.579-582
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• 2010

We propose a method of using the three dimensional characteristic information to classify the front environment in travelling by using the images captured by a CCD camera equipped on a mobile robot. So, this paper proposes the method of deciding the travelling direction of a mobile robot with using input images based upon the suggested algorithm by preprocessing, and verified the validity of the image information which are detected as obstacles by the analysis through neural network.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.33-40
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• 2006

This paper presents an efficient face recognition method using both first moment of image and eigenvector. First moment is a method for finding centroid of image, which is applied to exclude the needless backgrounds in the face recognitions by shitting to the centroid of face image. Eigenvector which are the basis images as face features, is extracted by principal component analysis(PCA). This is to improve the recognition performance by excluding the redundancy considering to second-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 60 face images(15 persons *4 scenes) of 320*243 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. In case of the 45 face images, the experimental results show that the recognition rate of the proposed methods is about 1.6 times and its the classification is about 5.6 times higher than conventional PCA without preprocessing. The city-block has been relatively achieved more an accurate classification than Euclidean or negative angle.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.1-9
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• 2003

The CNN-UM algorithm which performs the analog parallel subtraction of images has been developed and its application study to the moving target detection has been done. The CNN-UM is the state of the art computation architecture with high computational potential of analog parallel processing. It is one of the strong candidates for the next generation of computing system which fulfills requirement of the real-time image processing. One weakness of the CNN-UM is that its analog parallel processing function is not fully utilized for the inter frame processing. If two subsequent image frames are superimposed with opposite signs on identical capacitors for short time period, the analog subtraction between them is achieved. The Principle of such temporal inter-frame processing algorithm has been described and its mathematical analysis has been done. Practical usefulness of the proposed algorithm has also been verified through the application for moving target detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.396-402
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• 2021

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.877-880
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• 2006

기존의 수동적인 차량관리 시스템은 관리자가 정해진 위치에 상시 대기하여 관리를 해주어야 하며 아날로그 녹화 시스템의 화질저하와 저장공간의 부족이라는 단점이 있으므로 생산성이나 관리적으로 크게 비효율적이다. 본 논문에서는 이러한 아날로그적인 방법을 탈피하여 영상처리 기법을 이용해 보안카메라에서 받아온 영상정보를 이용해 차량정보를 파악하고 실시간으로 처리하며 Web이나 PDA등 디지털 이동매체에 접속하여 이동환경에서 활용할 수 있도록 통합 시스템을 구축하는 것을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.677-680
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• 2006

기존의 수동적인 차량관리 시스템은 관리자가 정해진 위치에 상시 대기하여 관리를 해주어야 하며 아날로그 녹화 시스템의 화질저하와 저장공간의 부족이라는 단점이 있으므로 생산성이나 관리적으로 크게 비효율적이다. 본 논문에서는 이러한 아날로그적인 방법을 탈피하여 영상처리 기법을 이용해 보안카메라에서 받아온 영상정보를 이용해 차량정보를 파악하고 실시간으로 처리하며 web이나 PDA등 디지털 이동매체에 접속하여 이동환경에서 활용할 수 있도록 통합 시스템을 구축하는 것을 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.143-144
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• 2019

자율 주행 차량의 상용화를 위해서는 차량의 정교한 위치 추정이 필수적이다 특히 실내공간의 경우 다중 경로 등 복잡한 경로를 주행 중인 차량의 위치를 추적해야 한다. 이 경우 정밀한 위치 추정을 위해 이동체가 주행하는 경로를 정확히 판별하는 것이 필수적이다. 본 논문에서는 다중 경로가 존재하는 복잡한 실내공간을 주행하는 이동체의 경로 추정을 위해 딥러닝 기법을 이용한다. 특히 딥러닝 기법이 주행 차량의 영상 정보를 활용하는 방식을 기술한다. 본 논문에서 딥러닝 방식은 주행 차량의 영상 정보를 이용하여 이동체가 주행하게 될 경로를 예측한다. 모의실험은 적용된 딥러닝 방식이 이동체의 주행 경로를 정확하게 예측함을 보인다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1132-1142
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• 1991

This paper proposed a method of feature parameter extraction for shape information analysis of moving object. In the 2-D plane, moving object are extracted by the difference method. Feature parameters of moving object are chosen area, perimeter, a/p ratio, vertex, x/y ratio. We changed brightness variation from the range of 600Lux to the 1400Lux and then determined Permissible Error range of feature parameter due to the brightness variation. So as to verify the validity of proposed method, experiment are performed with a toy car and it's results showed that decision error was less than 6%.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1028-1030
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• 2008

HSDPA, Wibro 의 서비스를 시작으로 제 3 세대 이동통신망이 상용화 서비스가 시작되었다. 하지만 3 세대 이동통신 서비스는 아직 사용자에게 기존의 서비스보다 향상된 서비스를 제공하지 못하고 있다. 이동성지원 및 전송방식 측면에서의 발전은 이루었지만 그에 따른 서비스를 제공하지 못했기 때문이다. 다른 한편으로 현재 서비스되고 있는 TPEG 은 서비스의 초기 단계로 버스나 택시에 GPS 칩이나 RF 칩을 장착해 정보를 수집하여 사용자에게 정량화된 방식으로 정보를 제공하고 있다. 본 논문에서는 이러한 3 세대이동통신망의 서비스 문제점과 기존의 TPEG 서비스의 문제점을 동시에 해결 할 수 있으며, 실시간으로 다수의 차량으로부터 영상과 위치정보를 전송 받을 수 있는 서비스를 개발 하였다. 제안된 서비스는 웹 카메라와 GPS 수신기, UMPC, 3 세대이동통신을 할 수 있는 모뎀를 이용하여 전송하며 일반적인 웹브라우저가 설치된 컴퓨터에서 수신한다.