• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.3
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• pp.78-78
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• 1998

The purpose of this research is to develop a CAD-based program for data analysis of digital elevation model(DEM) on the aspect of landscape assessment. When handling DEM data as a visual simulation of topographic landscape, it is basic interest to analyze visible area and visualize visual sensitivity distributions. In reference with landscape assessment, more intuitive and interactive visualizing tools are needed, specially in area of visual approach. For adaptability to landscape assessment, algorithmic approaches to visibility analysis and concepts for visual sensitivity calculation in this study were based on processing techniques of entity data control functions used in AutoCAD drawing database. Also, for the purpose of quantitative analysis, grid-type 3DFACE entities were adopted as mesh unit of DEM structure. Developed programs are composed of main part named VSI written in AutoLISP and two of interface modules written in dialog control language(DCL0 for user-oriented interactive usage. Definitions of camera points(view points) and target points(or observed area) are available alternatively in combined methods of representing scenic landscape, scenery, and sequential landscape. In the case of scene landscape(single camera to fixed target point), only visibility analysis in available. And total visibility, frequency of cumulative visibility, and visual sensitivity analysis are available in other cases. Visual sensitivity was thought as view angle(3 dimensional observed visual area) and the strengths were classified in user defined level referring to statistical characteristics of distribution. Visibility analysis routine of the VSI was proved to be more effective in the accuracy and time comparing with similar modules of existing AutoCAD third utility.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1553-1559
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• 2017

This paper proposes a lane departure detection algorithm using a single camera equipped in front of a vehicle. The proposed algorithm generates a partial top-view image for a small ROI (region of interest) designated on the top-view space form the image acquired by the camera, detects lanes on the small partial top-view image, and makes a decision on the lane departure by checking overlap between the pre-assigned virtual vehicle and the detected lanes. The proposed algorithm also includes the removal of lines occurred by road symbols (noises) disturbing the lane departure detection between lanes and the prediction of lost lanes using lane information of previous fames. In lane departure detection test using real road videos, the proposed algorithm makes the right decision of 99.0% in lane keeping conditions and 94.7% in lane departure conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4108-4114
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• 2015

An image acquisition by using an optical mirror is called as a catadioptric method. The catadioptric imaging method is generally used for acquisition of 360-degree all directional visual information in an image. An exemplar omnidirectional optical mirror is the bowl-shaped hyperbolic mirror. In this paper, a single camera omnidirectional stereo imaging method is studied with an additional concave lens. It is possible to obtain 3 dimensional coordinates of environmental objects from the omnidirectional stereo image by matching the stereo image having different view points. The omnidirectional stereo imaging system in this paper is cost-effective and relatively easy for correspondence matching because of consistent camera intrinsic parameters in the stereo image. The parameters of the imaging system are extracted through 3-step calibration and the performance for 3-dimensional coordinates reconstruction is verified through experiments in this paper. Measurable range of the proposed imaging system is also presented by depth-resolution analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.562-573
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• 2002

In this paper, we propose a method for measuring the volume of packages for the efficient handling of the packages. Using the geometrical characteristics of the rectangular parallelepiped type objects, the method measures the volume of packages with one camera only in real time. In preprocessing of volume measurement, the method extracts outer lines of the object and then crossing points of the lines as feature points or vertexes. From these cross points(-feature points-), the volume of the package is calculated. Compared to the direct feature extraction, the proposed method shows especially the blurring robust result by using the line for feature extraction. Additionally, the method can get the stable result by considering object's direction. From experimental results, it is demonstrated that this method is very effective for the real time volume measurement of the rectangular parallelepiped.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.453-454
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• 2003

Most research materials (data), which are used for the study of digital mapping and digital elevation model (DEM) in the field of Remote Sensing and Aerial Photogrammetry are aerial photographs and satellite images. Additionally, they are also used for National land mapping, National land management, environment management, military purposes, resource exploration and Earth surface analysis etc. Although aerial photographs have high resolution, the data, which they contain, are not used for environment exploration that requires continuous observation because of problems caused by its coastline, as well as single - spectral and long-term periodic image. In addition to this, they are difficult to interpret precisely because Satellite Images are influenced by atmospheric phenomena at the time of photographing, and have by far much lower resolution than existing aerial photographs, while they have a great practical usability because they are mulitispectral images. The PKNU 2 is an aerial photographing system that is made to compensate with the weak points of existing aerial photograph and satellite images. It is able to take pictures of very high resolution using a color digital camera with 6 million pixels and a color infrared camera, and can take perpendicular photographs because PKNU 2 system has equipment that makes the cameras stay level. Moreover, it is very cheap to take pictures by using super light aircraft as a platform. It has much higher resolution than exiting aerial photographs and satellite images because it flies at a low altitude about 800m. The PKNU 2 can obtain multispectral images of visible to near infrared band so that it is good to manage environment and to make a classified diagram of vegetation.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.125-137
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• 2004

In case of using image sequence taken from a moving camera along a road in an urban area, general video mosaicing technique based on a single baseline cannot create 2-D image mosaics. To solve the drawback, this paper proposed a new image mosaicing technique through 3-D multi-baselines that can create image mosaics in 3-D space. The core of the proposed method is that each image frame has a dependent baseline, an equation of first order, calculated by using ground control point (GCP) of optical flows. The proposed algorithm consists of 4 steps: calculation of optical flows using hierarchical strategy, calculation of camera exterior orientation, determination of multi-baselines, and seamless image mosaics. This paper realized and showed the proposed algorithm that can create efficient image mosaics in 3-D space from real image sequence.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.263-272
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• 2008

The proposed system can achieve more safety of RFID system with the 2-step authentication procedures for the enhancement about the security of general RFID systems. After it has authenticated RFID tag, additionally, the proposed system extract the characteristic information in the user image for acquisition of the additional authentication information of the user with the camera. In this paper, the system which was proposed more enforce the security of the automatic entrance and exit authentication system with the cognitive characters of RFID tag and the extracted characteristic information of the user image through the camera. The RFID system which use the active tag and reader with 2.4GHz bandwidth can recognize the tag of RFID in the various output manner. Additionally, when the RFID system have errors. the characteristic information of the user image is designed to replace the RFID system as it compare with the similarity of the color, outline and input image information which was recorded to the database previously. In the experimental result, the system can acquire more exact results as compared with the single authentication system when it using RFID tag and the information of color characteristics.

• Journal of Korean Academy of Oral Health
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• v.41 no.4
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• pp.262-266
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• 2017

Objectives: The aim of this study was to compare Plaque Percent Index (PPI), calculated by Patient Hygiene Performance Index (PHPI), Rustogi's modification of the Navy Plaque Index (RMNPI), and the Quigley & Hein Plaque Index (QHPI), with visual assessment. Methods: Ninety-six subjects, aged between 30-65 years, were examined; twenty subjects were included in the final analysis. The subjects' teeth were stained and photographed. Dental coloring and intraoral camera photography were performed by a single examiner. The oral images obtained were analyzed using Image J to measure the area of dental plaque. The values of PHPI, RMNPI, and QHPI were calculated twice. Statistical analyses were performed using descriptive statistics, chi-square test, and Pearson's correlation coefficient. Results: The results of the correlation analyses of PPI with PHPI, QHPI, and RMNPI were as follows: for PHPI, the correlation coefficient (r)=0.584; for QHPI, r=0.689; and for RMNPI, r=0.729. Further, the kappa indices of PHPI, QHPI, and RMNPI were 0.810, 0.677, and 0.590 respectively. Conclusions: Among RMNPI, QHPI, and PHPI dental plaque indices, RMNPI and QHPI showed a high degree of correlation with the actual stained dental plaque area; on the other hand, PHPI showed the highest kappa index.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1462-1467
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• 2018

A number of algorithms for improving the image quality of low light images have been studied using a single image or multiple exposure images. The low light image is low in contrast and has a large amount of noise, which limits the identification of information of the subject. This paper proposes the hardware design of algorithms that improve the quality of low light image using 2 multiple exposure images taken with a dual camera. The proposed hardware structure is designed in real time processing in a way that does not use frame memory and line memory using transfer function. The proposed hardware design has been designed using Verilog and validated in Modelsim. Finally, when the proposed algorithm is implemented on FPGA using xc7z045-2ffg900 as the target board, the maximum operating frequency is 167.617MHz. When the image size is 1920x1080, the total clock cycle time is 2,076,601 and can be processed in real time at 80.7fps.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.581-596
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• 2020

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying.