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An Efficient User Interest Region Stitching Method using the RANSAC Algorithm
http://doi.org/10.5626/JOK.2019.46.10.1025
Recently, with the emergence of 5G technology capable of high-capacity wireless data transmission, technologies using 360-degree virtual reality images have attracted attention, and therefore, interest in image stitching is increasing. In this paper, we proposed a user"s interest region matching method using the RANSAC algorithm. The proposed method uses the RANSAC algorithm and assigns a high weight to the region of interest selected by the user to perform image matching. It can be selectively performed in regions requiring natural stitching. The corresponding points included in the region of interest are set to have high weight and are necessarily included in the sample selection of the RANSAC algorithm. Additionally, the degree of matching to a specific region can be adjusted depending on whether several feature points are included. The method of interest region matching consists of setting the region of interest, increasing the weights of the corresponding points in the region of interest, creating the model using the RANSAC algorithm, and setting the inliers and outliers of the corresponding points using the model. The results of this study confirmed that users can get results approximating reality by performing matching based on points corresponding to selected regions of interest.
Anomaly Detection Analysis using Repository based on Inverted Index
Jumi Park, Weduke Cho, Kangseok Kim
http://doi.org/10.5626/JOK.2018.45.3.294
With the emergence of the new service industry due to the development of information and communication technology, cyber space risks such as personal information infringement and industrial confidentiality leakage have diversified, and the security problem has emerged as a critical issue. In this paper, we propose a behavior-based anomaly detection method that is suitable for real-time and large-volume data analysis technology. We show that the proposed detection method is superior to existing signature security countermeasures that are based on large-capacity user log data according to in-company personal information abuse and internal information leakage. As the proposed behavior-based anomaly detection method requires a technique for processing large amounts of data, a real-time search engine is used, called Elasticsearch, which is based on an inverted index. In addition, statistical based frequency analysis and preprocessing were performed for data analysis, and the DBSCAN algorithm, which is a density based clustering method, was applied to classify abnormal data with an example for easy analysis through visualization. Unlike the existing anomaly detection system, the proposed behavior-based anomaly detection technique is promising as it enables anomaly detection analysis without the need to set the threshold value separately, and was proposed from a statistical perspective.
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