• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.108-114
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• 2007

This paper presented an image reconstruction method based on the original capsule endoscopy photos yielding a 2-D image for faster diagnosis proposes. The proposed method constructed a 3-D intestine model using the massive images obtained from the capsule endoscope. It merged all images and completed a 3-D model of an intestine. This 3-D model was reformed as a 2-D plane image showing the inner side of the entire intestine. The proposed image composition was evaluated by the 3-D simulator, OpenGL. This approach was demonstrated successfully. A physician can find the location of a disease at a glance because the composite image provided an easy-to-understand view to show the patient's intestine and thereby shorten diagnosis time.

• Journal of the Korea Furniture Society
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• v.25 no.4
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• pp.338-344
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• 2014

Modern furniture design is advancing by putting value on individual sensitivity. Accordingly, this study will focus on optical art, which can give various changes in people's sensitivity. Optical art was actively applied in various fields of image, photograph, fashion, textile, accessory and interior from the 1950s till the 1960s. Optical art realized by Victor Vasarely (1908~) opened a new trend in art by accurately realizing concise and precise expressions. This study analyzed the impact of pattern expression on sensitivity in optical art and suggested a method, which can spatiotemporally maximize the emotional change by combining optical art with the form of furniture. Modern furniture design is changing toward the direction of fitting to the propensity and emotional taste of an individual. Accordingly, this study analyzed the emotional expression felt in the furniture modeling works featured by concise, straight and standardized rectangular shape. Based on the result of analysis, this study would like to suggest a method to utilize optical pattern as a means for emotional design, with which people are able to perform various emotional expressions while keeping the function and form of furniture.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.6
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• pp.1482-1489
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• 1998

This paper aims to extract characteristics of the spectrum of fingerprint image and to apply them to image enhancement techniques in spatial frequency domain. Based on 1$\times$64 window as a processing unit and the different record lengths(32, 16, 8), the estimate of power spectrum density for each length was made. Each acquired spectrum characteristics was applied to the re-synthesis process of the fingerprint image, an improved gray scale image was obtained. In order to select an optimal predictor and the Huffman table for the fingerprint iamge, the lossless JPEG algorithm was used. Experiments were performed for extracting distribution characteristics for the each of 7 predictors from the fingerprint image and modeling processes, and the result was applied to the data compression algorithm and the selection of the optimal predictor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.19-30
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• 2007

Modeling 3D buildings is an essential process to revive the real world into a computer. There are two ways to create a 3D building model. The first method is to use the building layer of 1:1000 digital maps based on high density point data gained from airborne laser surveying. The second method is to use LiDAR point data with digital images achieved with LiDAR. In this research we tested one sheet area of 1:1000 digital map with both methods to process a 3D building model. We have developed a process, analyzed quantitatively and evaluated the efficiency, accuracy, and reality. The resulted differed depending on the buildings shape. The first method was effective on simple buildings, and the second method was effective on complicated buildings. Also, we evaluated the accuracy of the produced model. Comparing the 3D building based on LiDAR data and digital image with digital maps, the horizontal accuracy was within ${\pm}50cm$. From the above we derived a conclusion that 3D building modeling is more effective when it is based on LiDAR data and digital maps. Using produced 3D building modeling data, we will be utilized as digital contents in various fields like 3D GIS, U-City, telematics, navigation, virtual reality and games etc.

• Aerospace Engineering and Technology
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• v.7 no.2
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• pp.187-195
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• 2008

Generation of accurate ground coordinates from high resolution satellite image are becoming increasingly of interest. The primary focus of this paper is to compute satellite direct sensor model (DSM) and rational function model (RFM) for accurate generation of ground coordinates from high resolution satellite images. Being based on this we presented an algorithm to be able to efficiently ground coordinates about large area with introducing RFM(rational function model) method applied to rigorous sensor modeling standing on basis of satellite orbit dynamics and collinearity equation, and sensor modeling of high-resolution satellite data like IKONOS, QuickBird, KOMPSAT-2 and others. The general high resolution satellite measures the position, velocity and attitude data of satellite using star, gyro, and GPS sensors.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.8-16
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• 2021

The tourism industry is influenced by a large number of factors that affect the development scenarios of the tourism in different ways. At the same time, tourism is an important component of the national economy of any state, forms its image, investment attractiveness, is a source of income and a stimulus for business development. The aim of the article is to conduct an empirical study to identify the importance of cognitive determinants in the development of tourism. The study used general and special methods: systems analysis, synthesis, grouping, systematization, cognitive modeling, cognitive map, pulse method, predictive extrapolation. Target factors, indicators, and control factors influencing the development of tourism in Ukraine are determined and a cognitive model is built, which graphically reflects the nature of the influence of these factors. Four main scenarios of the Ukrainian tourism industry are established on the basis of creating a matrix of adjacency of an oriented graph and forecast modeling based on a scenario approach. The practical significance of the obtained results lies in the possibility of their use to forecast the prospects of tourism development in Ukraine, the definition of state policy to support the industry that will promote international and domestic tourism.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.73-83
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• 2008

For spatial analysis and decision-making based on territorial and urban information, technologies on 3D GIS with digital image data and photo-realistic 3D image models to visualize 3D modeling are being rapidly developed. Currently, satellite images, aerial images and aerial LiDAR data are mostly used to build 3D models and textures from oblique aerial photographs or terrestrial photographs are used to create 3D image models. However, we are in need of quality 3D image models as current models cannot express topographic and features most elaborately and realistically. Thus, this study analyzed techniques to use aerial photographs, aerial LiDAR, terrestrial photographs and terrestrial LiDAR to create a 3D image model with artificial features and special topographic that emphasize spatial accuracy, delicate depiction and photo-realistic imaging. A 3D image model with spatial accuracy and photographic texture was built to be served via 3D image map services systems on the Internet. As it was necessary to consider intended use and display scale when building 3D image models, in this study, we applied the concept of LoD(Level of Detail) to define 3D image model of buildings in five levels and established the models by following the levels.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.