• Journal of Information Processing Systems
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• 제16권1호
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• pp.155-170
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• 2020

Passive ranging is a critical part of machine vision measurement. Most of passive ranging methods based on machine vision use binocular technology which need strict hardware conditions and lack of universality. To measure the distance of an object placed on horizontal plane, we present a passive ranging method based on monocular vision system by smartphone. Experimental results show that given the same abscissas, the ordinatesis of the image points linearly related to their actual imaging angles. According to this principle, we first establish a depth extraction model by assuming a linear function and substituting the actual imaging angles and ordinates of the special conjugate points into the linear function. The vertical distance of the target object to the optical axis is then calculated according to imaging principle of camera, and the passive ranging can be derived by depth and vertical distance to the optical axis of target object. Experimental results show that ranging by this method has a higher accuracy compare with others based on binocular vision system. The mean relative error of the depth measurement is 0.937% when the distance is within 3 m. When it is 3-10 m, the mean relative error is 1.71%. Compared with other methods based on monocular vision system, the method does not need to calibrate before ranging and avoids the error caused by data fitting.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.1103-1106
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• 2005

본 연구의 목적은 카메라를 통하여 획득한 이미지 정보를 캡쳐 후, 이를 분석하여 물체의 동작을 인식하는 Vision Box를 설계하는데 목적이 있다. 본 연구는 고객 즉, 사용자의 요구조건을 최대한 반영하여 구현하고자 하였다. 구현하고자 하는 Vision Box 시스템은 특별한 외부의 부가적인 센서를 사용하지 않고 카메라를 통하여 들어오는 화상 정보만을 분석하여 물체를 식별할 수 있도록 하였다. 그리고 PLC와의 통신과 원격지에서 Vision Box를 제어할 수 있는 방법도 지원할 수 있도록 하였다. 본 연구에서 제안한 Vision Box의 성능을 자동차 엔진패턴 검사를 통하여 검증할 수 있었으며 제안한 Vision Box 시스템이 다양한 산업분야에 적용될 수 있을 것이라 생각된다.

• 한국공작기계학회논문집
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• 제15권5호
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• pp.37-43
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• 2006

The target of this study is development of web based mold discrimination system by matching vision information with CAD database. The use of 2D vision image makes possible speedy mold discrimination from many databases. The image processing such as preprocessing, cleaning is done for obtaining vivid image with object information. The web-based system is a program which runs to exchange messages between a server and a client by making of ActiveX control and the result of mold discrimination is shown on web-browser. For effective feature classification and extraction, signature method is used to make sensible information from 2D data. As a result, the possibility of proposed system is shown as matching feature information from vision image with CAD database samples.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.881-884
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• 2000

Foveated vision has the big advantage of exhibiting a wide field of view, along with a high resolution fovea. However, in the case of using optical flow, foveated vision kas one demerit. The demerit is a concentrate of optical flow. For foveated vision, an object moves almost only around the center of the field. In this paper, we suggest how to segment motion of some objects, and how to discriminate a hand and another object. In the future, the method we suggested may be useful for recognizing human actions by foveated vision.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.836-841
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• 2004

This paper describes ASIC design for brightness revision control, A LED Pixel Matrix (LPM) design and LPM in natural color LED vision. A designed chip has 256 levels of gradation correspond to each Red, Green, Blue LED pixel respectively, which have received 8bit image data. In order to maintain color uniformity by reducing the original rank error of LED, we adjusted the specific character value 'a' and brightness revision value 'b' to pixel unit, module unit and LED vision respectively by brightness characteristic function with 'Y=aX+b'. In this paper, if designed custom chip and brightness revision control method are applied to manufacturing of natural color LED vision, we can obtain good quality of image. Furthermore, it may decrease the cost for manufacturing LED vision or installing the plants.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.77-80
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• 2022

Automatic Vision Inspection (AVI) systems automatically detect defect features and measure their sizes via camera vision. It has been populated because of the accuracy and consistency in terms of QC (Quality Control) of inspection processes. Also, it is important to predict the performance of an AVI to meet customer's specification in advance. AVI are usually suffered from false negative and positives. It can be overcome by providing extra information such as 3D depth information. Stereo vision processing has been popular for depth extraction of the 3D images from 2D images. However, stereo vision methods usually take long time to process. In this paper, retro optical system using reflectors is proposed and experimented to overcome the problem. The optical system extracts the depth without special SW processes. The vision sensor and optical components such as illumination and depth detecting module are integrated as a unit. The depth information can be extracted on real-time basis and utilized and can improve the performance of an AVI system.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1202-1208
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• 2022

Techniques based on computer vision are becoming increasingly important in construction safety monitoring. Using AI algorithms can automatically identify conceivable hazards and give feedback to stakeholders. However, the construction site remains various potential hazard situations during the project. Due to the site complexity, many visual devices simultaneously participate in the monitoring process. Therefore, it challenges developing and operating corresponding AI detection algorithms. Safety information resulting from computer vision needs to organize before delivering it to safety managers. This study proposes a framework for computer vision-aided construction safety monitoring using collaborative 4D BIM information to address this issue, called CSM4D. The suggested framework consists of two-module: (1) collaborative BIM information extraction module (CBIE) extracts the spatial-temporal information and potential hazard scenario of a specific activity; through that, Computer Vision-aid Safety Monitoring Module (CVSM) can apply accurate algorithms at the right workplace during the project. The proposed framework is expected to aid safety monitoring using computer vision and 4D BIM.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.767-771
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• 2003

In this study, stereo vision system is applied to a mobile manipulator for effective tasks. The robot can recognize a target and compute the position of the target using a stereo vision system. While a monocular vision system needs properties such as geometric shape of a target, a stereo vision system enables the robot to find the position of a target without additional information. Many algorithms have been studied and developed for an object recognition. However, most of these approaches have a disadvantage of the complexity of computations and they are inadequate for real-time visual servoing. However, color information is useful for simple recognition in real-time visual servoing. In this paper, we refer to about object recognition using colors, stereo matching method, recovery of 3D space and the visual servoing.

• 한국정보통신학회논문지
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• 제14권8호
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• pp.1767-1775
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• 2010

무인 운반설비의 자동화 시스템 개발의 한 부분으로써 Stereo vision system에 관한 많은 연구가 진행되고 있다. Stereo vision system에서는 영상을 통해 특정 물체를 검색하고 검색된 물체 정보를 기반으로 Edge를 추출하고, 추출된 Edge를 이용하여 물체의 위치적 특징을 찾고 무인크레인이 이동해야할 위치좌표를 전달한다. 본 논문에서는 실제 산업현장에 가장 보편적인 형상인 Slab와 Coil을 기준으로 두 대의 CCD camera를 이용하여 물체의 형상을 인식하고, 무인크레인의 Hookblock부분이 물체의 중심점을 찾는 알고리즘을 개발하였다. 본 논문에서는 Stereo vision system의 카메라 설치 위치에 따라 직교식과 수평식으로 2가지의 방식을 제안, 실험을 하였다. 본 논문에서 제안한 알고리즘은 무인 운반설비의 자동화 시스템 개발에 도움이 될 것으로 기대된다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.89-93
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• 2009

Embedded systems are becoming more popular as many embedded platforms have become more affordable. It offers a compact solution for many different problems including computer vision applications. Texture classification can be used to solve various problems, and implementing it in embedded platforms will help in deploying these applications into the market. This paper proposes to deploy the texture classification algorithms onto the embedded computer vision (ECV) platform. Two algorithms are compared; grey level co-occurrence matrices (GLCM) and Gabor filters. Experimental results show that raw GLCM on MATLAB could achieves 50ms, being the fastest algorithm on the PC platform. Classification speed achieved on PC and ECV platform, in C, is 43ms and 3708ms respectively. Raw GLCM could achieve only 90.86% accuracy compared to the combination feature (GLCM and Gabor filters) at 91.06% accuracy. Overall, evaluating all results in terms of classification speed and accuracy, raw GLCM is more suitable to be implemented onto the ECV platform.