• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.172-174
• /
• 2022

Logistics processing companies in Korea are mostly handling various types of products in and out. In order to process various types of products, the sorting business is performed by hand. In this paper, we propose a real-time QR code detection method using a vision sensor to achieve high efficiency with a small amount of manpower. The limiting system uses a vision sensor to process QR code recognition of logistics in real time. The proposed system can quickly identify a large number of QR codes through multiple recognition rather than QR code recognition, which is a single part of logistics. In the study, the system was actually implemented and verified, and multiple QR recognition was confirmed in the image through the vision center.

• Smart Structures and Systems
• /
• v.24 no.5
• /
• pp.617-630
• /
• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.89-93
• /
• 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.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.20 no.1
• /
• pp.1-6
• /
• 1983

To give the recognition capability of task objects by computer vision to a sensor-based robot system, an image digitizer and some basic software techniques were developed and repofted here. The image digitizer was developed with the CROMEMCO SYSTEM III microcomputer anti C.C.T.V. camera to convert the analog valued scene into digitized image which could be pro-cessed by a digital computer. Basic software techniques for the computer vision system were aimed at the recognition of 3-dimensional objects. Experiments with these techniques were carried out using the image of a cubicle which could be considered as typical simple 3-dimensional object.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1083-1097
• /
• 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
• /
• v.29 no.3
• /
• pp.275-280
• /
• 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%.

• Journal of Korea Multimedia Society
• /
• v.18 no.5
• /
• pp.583-598
• /
• 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.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.2
• /
• pp.95-101
• /
• 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
• /
• v.23 no.5
• /
• pp.193-206
• /
• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.153-156
• /
• 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.