• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.172-174
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• 2022

국내에서 물류를 처리하는 회사에서는 다양한 형태의 제품을 입출고 처리하고 있다. 다양한 형태의 제품을 처리하기 위해서 수작업으로 분류 업부를 수행하고 있다. 본 논문에서는 적은 인력으로 고효율을 내기 위해 비전 센서를 사용하는 실시간 QR코드 탐지 방법을 제안한다. 제한하는 시스템은 비전 센서를 사용하여 물류의 QR코드 인식을 실시간으로 처리가 가능하다. 제안하는 시스템은 물류의 단인 QR 코드 인식이 아닌 다중 인식을 통해서 다량의 QR 코드를 빠르게 파악할 수 있다. 연구에서는 시스템을 실제 구현하여 검증을 진행하여 비전 센터를 통해서 이미지에서 다중 QR 인식을 확인하였다.

• Smart Structures and Systems
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• 제24권5호
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• 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.

• 대한전자공학회논문지
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• 제20권1호
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• pp.1-6
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• 1983

시각 능력에 의한 작업 대상의 인식 기능을 갖는 로보트 시스템에 괼도한 image digiilzer와 기본적인 몇가지 소프트웨어 기법의 개발에 관한 연구 결과를 보고한다. Image digitizer는 CROMEMCO SYSTEM III 마이크로컴퓨터와 C.C.C.T.V. 카메라를 이용하여 개발하였으며, 소프트웨어 기법들은 3차원 물체의 인식에 중점을 두었는데, 이 기법들의 유용성을 간략한 물체인 직육면체에의 응용을 통해 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1083-1097
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• 2018

This paper presents a novel range-free localization algorithm based on quantum particle swarm optimization. The proposed algorithm is capable of estimating the distance between two non-neighboring sensors for multi-hop heterogeneous wireless sensor networks where all nodes' communication ranges are different. Firstly, we construct a new cumulative distribution function of expected hop progress for sensor nodes with different transmission capability. Then, the distance between any two nodes can be computed accurately and effectively by deriving the mathematical expectation of cumulative distribution function. Finally, quantum particle swarm optimization algorithm is used to improve the positioning accuracy. Simulation results show that the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes for heterogeneous wireless sensor networks.

• Journal of Biosystems Engineering
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• 제29권3호
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• pp.275-280
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• 2004

The main objective of this study was to develop and evaluate a model for estimating pigs weight using computer vision for improving the management in Korean swine farms in Korea. This research was carried out in two steps: 1) to find a model that relates the projection area with the weight of a pig; 2) to implement the model in a computer vision system mainly consisted of a monochrome CCD camera, a frame grabber and a computer system for estimating the weight of pigs in a non-contact, real-time manner. The model was developed under an important assumption there were no observable genetic differences among the pigs. The main results were: 1) The relationship between the projection area and the weight of pigs was W = 0.0569 ${\times}$ A - 32.585($R^2$ = 0.953), where W is the weight in kg; A is the projection area of a pig in $\textrm{cm}^2$; 2) The model could estimate the weight of pigs with an error less than 3.5%.

• 한국멀티미디어학회논문지
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• 제18권5호
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• pp.583-598
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• 2015

We need many assembly lines to produce industrial product such as automobiles that require a lot of composited parts. Big portion of such assembly line are still operated by manual works of human. Such manual works sometimes cause critical error that may produce artifacts. Also, once the assembly is completed, it is really hard to verify whether of not the product has some error. In this paper, for monitoring behaviors of manual human work in an assembly line automatically, we proposes a realtime hybrid monitoring system that combines 2D vision sensor tracking technique with 3D motion recognition sensors.

• 제어로봇시스템학회논문지
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• 제19권2호
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• pp.95-101
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• 2013

The paper investigates the geometric effect of landmarks to the navigation error in the landmark based 3D vision navigation and introduces the INS/Vision integrated navigation system considering its effect. The integrated system uses the vision navigation results taking into account the dilution of precision for landmark geometry. Also, the integrated system helps the vision navigation to consider it. An indirect filter with feedback structure is designed, in which the position and the attitude errors are measurements of the filter. Performance of the integrated system is evaluated through the computer simulations. Simulation results show that the proposed algorithm works well and that better performance can be expected when the error characteristics of vision navigation are considered.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.193-206
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• 2023

In recent years, and with the increased adoption of digital transformation and spending long hours in front of these devices, clinicians have observed that the prolonged use of visual display units (VDUs) can result in a certain symptom complex, which has been defined as computer vision syndrome (CVS). This syndrome has been affected by many causes, such as light refractive errors, poor computer design, workplace ergonomics, and a highly demanding visual task. This research focuses on eliminating one of CVSs, which is the eye dry syndrome caused by infrequent eye blink rate while using a smart device for a long time. This research attempt to find a limitation on the current tools. In addition, exploring the other use cases to utilize the solution based on each vertical and needs.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.153-156
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• 2005

Real-time monitoring and controlling manufacturing process is important because of the unexpected events. When unexpected event like mechanical trouble occurs, prior plan becomes unacceptable and a new schedule must be generated though manufacturing schedule is already decided for order. Regenerating the whole schedule, however, spends much time and cost. Thus automated system which monitors and controls manufacturing process is required. In this paper, we present a system which uses radio-frequency identification and computer vision system. The system collect real-time information about manufacturing conditions and generates new schedule quickly with those information.