• 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.

• Management & Information Systems Review
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• v.37 no.2
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• pp.149-167
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• 2018

The purpose of this study is to analyze the performance of Virtual Desktop Infrastructure(VDI) adoption. VDI performance was measured by IS manager (system quality, security, and managerial operation) and business user (usability, access, and user satisfaction). The survey questionnaires were developed for measuring VDI performance. 84 data samples were collected from the companies that had adopted cloud-based VDI. This research model was verified by Smart-PLS and SPSS. The research findings were as follows: First, the companies using VDI experienced actual performance, but they did not attain their expectation. Second, as results of comparing between IS managers and business users, IS administrators had considerably higher performance than business users, which indicates that there were big differences in performance perception among users. Compared with prior research such as technical trend, system construction, and performance improvement, this study has the following implications. First, by comparing the expected performance with the actual performance of the companies that have implemented and operating VDI, it was suggested how a company that wants to adopt VDI can manage the expectation level of VDI and achieve higher actual performance. Second, because the perception of VDI performance differs between business users and system managers, it is meaningful that a fair evaluation of VDI performance requires a balanced consideration of business users and system managers.