• Journal of Digital Convergence
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• v.18 no.5
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• pp.425-432
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• 2020

Stereoscopic imaging is an important branch in the field of digital imaging, and has grown significantly over the last two decades through research and development. Hologram projection technology based on stereoscopic technology has been a social issue since the development of the Hungarian physicist Daniel Garber in 1947. It is becoming more and more important as stereoscopic works using similar technologies are applied to various fields. This study analyzed content cases using classical and creative stereoscopic technology from the perspective of digital image content design and explored the multiprotocol direction of holographic projection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.462-468
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• 2010

In this paper, the new efficient drive control technology based on the conventional full-color electric display board image processing method is proposed. This technology can be applied to high-fidelity large size panel TV and LCD Display, and makes high-fidelity-display-drive control possible in those. The proposed drive control technology can strengthen the image processing function of the conventional technology. Also, the automatic or manual adjustment of contrast, bright, tint, color, gamma revision, etc. can help to achieve high-fidelity vision. This technology can be adopted with lower price.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.199-206
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• 2012

The New IT industry based on Brain Science, a new industry combined with brain science and IT, will develop new IT services based on Brain Science. And this industry will become the new growth engine industry. This study examines the vision/strategies of policy for the New IT Convergence Industry based on Brain Science. Therefore this article's purposes are below; first, structuring the ecosystem for New IT industry based on Brain Science, second, classifying the service types of this industry, and last, suggesting of promotion policy for this industry.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.15-20
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• 2015

After the US government declared 3D printing technology a next-generation manufacturing technology, there have been many practical studies conducted to expand 3D printing technology to manufacturing technologies, called AMERICA MAKES. In particular, the Keck Center, located at the University of Texas at El Paso, has studied techniques for easily combing the 3D stacking process with space mobility and expanded these techniques to simultaneous staking techniques for multiple materials. Additionally, it developed convergence manufacturing techniques, such as direct inking techniques, in order to produce a module structure that combines electronic circuits and components, such as CUBESET. However, in these studies, it is impossible to develop a unified system using traditional independent through simple sequencing connections. This is because there are many problems in the integration between the stacking modeling of 3D printers and post-machining, such as thermal deformations, the precision accuracy of 3D printers, and independently driven coordinate problems among process systems. Therefore, in this paper, the integration method is suggested, which combines these 3D printers and subsequent machining process systems through an Internet-based cloud. Additionally, the sequential integrated system of a 3D printer, an NC milling machine, machine vision, and direct inking are realized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.562-568
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• 2014

With the development of computer technology, augmented reality (Augmented Reality, AR) technology in the future, one of the main directions of development of human interface technology is emerging. On augmented reality based on the design and implementation of a digital business card system. In this paper, a Smartphone is simply information through recognizable digital business card contains information about the system. Digital business card system is compared to the way existing hardware in a way visually-based high precision. In addition, registered as a 3D computer vision of augmented reality technology skills and real-world situations convergence technology for research. Future research, 3D electronic map for Smartphone apps as of the application user interface on the side for research is needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.344-352
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• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.663-668
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• 2003

This paper presents a new observation system which is useful to observe the scene of the remote controlled robot vision. This system is composed of a motionless camera and head motion detector with a motion sensor. The motionless camera has a fish eye lens and is for observing a hemisphere space. The head motion detector has a motion sensor is for defining an arbitrary subspace of the hemisphere space from fish eye lens. Thus processing the angular information from the motion sensor appropriately, the direction of face is estimated. However, since the fisheye image is distorted, it is unclear image. The partial domain of a fish eye image is selected by head motion, and this is converted to perspective image. However, since this conversion enlarges the original image spatially and is based on discrete data, crevice is generated in the converted image. To solve this problem, interpolation based on an intensity of the image is performed for the crevice in the converted image (space problem). This paper provides the experimental results of the proposed observation system with the head motion detector and perspective image conversion using the proposed conversion and interpolation methods, and the adequacy and improving point of the proposed techniques are discussed.

• Information and Communications Magazine
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• v.34 no.2
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• pp.12-19
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• 2017

The LED technology advancement introduce cuttingedge technology on Internet of Things (IoT) to connect the physical world to the digital realm, using digital smart lighting infrastructure called Internet of light (IoL). This paper proposes an Integrated IoT agent on networked smart LED lighting controller called IoL Filed Gateway using lighting infrastructure in which a lighting system that can connect to a network and can be monitored and controlled from a centralized system or via the cloud. The IoL Field Gateway defines new world of smart connected intelligence, lighting can become an integral and responsive part of everyday human life environments. The proposed connected lighting gateway uses the concept of multi-hop ad hoc network using visible light communication (VLC) with RF wireless technologies and Wired PLC (Power Line Communication). This connectivity and intelligence integrated into LED-based luminaires form the backbone of smart buildings and cities and make the Internet of Things (IoT) vision feasible and enables the lighting administrator can control numerous lightings easily and visitors can get visual information from the lightings with their smart devices. The proposed IoL gateway design is emulated on Arduino based HW platform with VLC, RF, and PLC connectivity and evaluated with four sensor interface.