• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1057-1068
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• 2022

This paper compares the combination performance of feature point-based matching algorithms as a study to confirm the matching possibility between image taken by a user and a virtual texture image with the goal of developing mobile-based real-time image positioning technology. The feature based matching algorithm includes process of extracting features, calculating descriptors, matching features from both images, and finally eliminating mismatched features. At this time, for matching algorithm combination, we combined the process of extracting features and the process of calculating descriptors in the same or different matching algorithm respectively. V-World 3D desktop was used for the virtual indoor texture image. Currently, V-World 3D desktop is reinforced with details such as vertical and horizontal protrusions and dents. In addition, levels with real image textures. Using this, we constructed dataset with virtual indoor texture data as a reference image, and real image shooting at the same location as a target image. After constructing dataset, matching success rate and matching processing time were measured, and based on this, matching algorithm combination was determined for matching real image with virtual image. In this study, based on the characteristics of each matching technique, the matching algorithm was combined and applied to the constructed dataset to confirm the applicability, and performance comparison was also performed when the rotation was additionally considered. As a result of study, it was confirmed that the combination of Scale Invariant Feature Transform (SIFT)'s feature and descriptor detection had the highest matching success rate, but matching processing time was longest. And in the case of Features from Accelerated Segment Test (FAST)'s feature detector and Oriented FAST and Rotated BRIEF (ORB)'s descriptor calculation, the matching success rate was similar to that of SIFT-SIFT combination, while matching processing time was short. Furthermore, in case of FAST-ORB, it was confirmed that the matching performance was superior even when 10° rotation was applied to the dataset. Therefore, it was confirmed that the matching algorithm of FAST-ORB combination could be suitable for matching between virtual texture image and real image.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.147-158
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• 2021

This study was conducted seven times from 2007 to the 2019 Women's World Handball Championships to analyze and strengthen the strength of the Korean women's handball team through the analysis of the top five countries' strengths. Among the 41 national teams participating in the World Women's Handball Championship, a total of five national teams, including the Netherlands, Norway, Russia, Spain, and France, were selected for the final study. Among the records provided by the International Handball Federation (IHF), the ranking was selected by analyzing the competition records of 41 participating countries, and technical statistics and frequency analysis were conducted using the SPSS/PC+ Ver21.0 program. based on the accumulated records of the top five women's handball competitions, handball attack and defense strategies that can make up for the inferiority in future physical conditions are needed and detailed follow-up studies are needed. Also, we hope to use it as a basic resource for improving the performance of Korean women's handball players and to play a key role in enhancing the level of women's handball at the 2021 Tokyo Olympics.