• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.693-701
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• 2010

In this paper, it is proposed that an attitude and position estimation algorithm based on a stereo camera system for a helmet tracker. Stereo camera system consists of two CCD camera, a helmet, infrared LEDs and a frame grabber. Fifteen infrared LEDs are feature points which are used to determine the attitude and position of the helmet. These features are arranged in triangle pattern with different distance on the helmet. Vision-based the attitude and position algorithm consists of feature segmentation, projective reconstruction, model indexing and attitude estimation. In this paper, the attitude estimation algorithm using UQ (Unit Quaternion) is proposed. The UQ guarantee that the rotation matrix is a unitary matrix. The performance of presented algorithm is verified by simulation and experiment.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.511-513
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• 1998

This paper describes a full color stereoscopic video display system using a LCD shutter. Human apprehends the world with a natural stereo vision. The left eye sees through a slightly different perspective from the right eye; proposed vision system combines two images into a single image that has stereo depth. That is, when the left image is on the screen, the left shutter opens and the right shutter closes - and vice versa. The LCD shutter channels the left image to the left eye, and the right image to the right eye. The brain then fuses the stereo pair into a single high-resolution, flicker-free 3D image. The designed vision system is a real-time system that shows stereoscopic images without the loss of image information from video cameras.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.1
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• pp.77-88
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• 1993

In this paper, we propose a stereo vision system to solve correspondence problem with large disparity and sudden change in environment which result from small distance between camera and working objects. First of all, a specific feature is divided by predfined elementary feature. And then these are combined to obtain coded data for solving correspondence problem. We use Neural Network to extract elementary features from specific feature and to have adaptability to noise and some change of the shape. Fourier transformation and Log-polar mapping are used for obtaining appropriate Neural Network input data which has a shift, scale, and rotation invariability. Finally, we use associative memory to obtain coded data of the specific feature from the combination of elementary features. In spite of specific feature with some variation in shapes, we could obtain satisfactory 3-dimensional data from corresponded codes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.207-212
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• 2008

최근 공장 자동화가 보편화됨에 따라 무인 운반설비의 자동화 시스템 개발의 한 부분으로써 Stereo Vision 장치를 이용하여 입력된 영상의 에지(Edge)를 추출하고, 추출된 에지를 이용하여 물체의 위치적 특징을 찾고 무인크레인이 이동해야할 위치좌표를 전달한다. 본 연구에서는 실제 산업현장에 가장 보편적인 형상인 판재와 원통을 기준으로 CCD 카메라 2대를 이용하여 물체의 형상을 인식하고, 그 물체의 중심점을 찾는 알고리즘을 제안하였다. 우선 에지를 추출하고 사용자의 선택에 따라 추출된 에지의 특징을 판별하여 판재와 원통을 구분하여 원하는 물체의 위치정보를 찾아낸다. 본 연구는 무인 운반설비의 자동화 시스템 개발에 도움이 될 것으로 기대된다.

• Current Optics and Photonics
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• v.2 no.4
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• pp.348-355
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• 2018

How to measure the creepage distance of an insulator quickly and accurately is a problem for the power industry at present, and the noticeable concern is that the high voltage insulation equipment cannot be measured online in the charged state. In view of this situation, we develop an on-line measurement system of creepage distance for high voltage insulators based on binocular stereo vision. We have proposed a method of generating linear structured light using a conical off-axis mirror. The feasibility and effect of two ways to solve the interference problem of strong sunlight have been discussed, one way is to use bandpass filters to enhance the contrast ratio of linear structured light in the images, and the other way is to process the images with adaptive threshold segmentation and feature point extraction. After the system is calibrated, we tested the measurement error of the on-line measurement system with a composite insulator sample. Experimental results show that the maximum relative error is 1.45% and the average relative error is 0.69%, which satisfies the task requirement of not more than 5% of the maximum relative error.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.10
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• pp.14-21
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• 2006

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.264-264
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• 2000

In this paper, we propose a method to automatically extract stable visual landmarks from observed data for a mobile robot with stereo vision system. The robot selects as stable landmarks vertical line segments which are distinct and on planar surfaces, because they are expected to be observed reliably from various view-points. When the robot moves, it uses several, less uncertain landmarks for estimating its motion. Experimental results in real scenes show the validity of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.729-734
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• 2005

One of the important research issues in mobile robot is how to detect the 3D environment fast and accurately, and recognize it. Sensing methods of utilizing laser structured light and/or stereo vision are representatively used among a number of methodologies developed to date. However, the methods are still in need of achieving high accuracy and reliability to be used for real world environments. In this paper to implement a new robotic environmental sensing algorithm is presented by combining the information between intensity image and that of laser structured light image. To see how effectively the algorithm applied to real environments, we developed a sensor system that can be mounted on a mobile robot and tested performance for a series of environments.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1678-1680
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• 2002

Stereo matching technique is used in many practical fields like satellite image analysis and computer vision. In this paper, we suggest a method to extract a target object image from a complicated background. For example, human face image can be extracted from random background. This method can be applied to computer vision such as security system, dressing simulation by use of extracted human face, 3D modeling, and security system. Many researches about stereo matching have been performed. Conventional approaches can be categorized into area-based and feature-based method. In this paper, we start from area-based method and apply area tracking using scanning window. Coarse depth information is used for area merging process using area searching data. Finally, we produce a target object image.