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RISS 인기검색어
- 검색키워드
- 저자 : Dalaijargal Purevsuren (검색결과 2 건)
- 무료
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Nyamlkhagva Sengee,Dalaijargal Purevsuren,Tserennadmid Tumurbaatar 한국멀티미디어학회 2022 The journal of multimedia information system Vol.9 No.2
In this study, we aimed to illustrate that the thresholding method gives different results when tested on the original and the refined histograms. We use the global thresholding method, the well-known image segmentation method for separating objects and background from the image, and the refined histogram is created by the neighborhood distinction metric. If the original histogram of an image has some large bins which occupy the most density of whole intensity distribution, it is a problem for global methods such as segmentation and contrast enhancement. We refined the histogram to overcome the big bin problem in which sub-bins are created from big bins based on distinction metric. We suggest the refined histogram for preprocessing of thresholding in order to reduce the big bin problem. In the test, we use Otsu and medianbased thresholding techniques and experimental results prove that their results on the refined histograms are more effective compared with the original ones.
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Collective Betweenness Centrality in Networks
Gantulga Gombojav,Dalaijargal Purevsuren,Nyamlkhagva Sengee 한국멀티미디어학회 2022 The journal of multimedia information system Vol.9 No.2
The shortest path betweenness value of a node quantifies the amount of information passing through the node when all the pairs of nodes in the network exchange information in full capacity measured by the number of the shortest paths between the pairs assuming that the information travels in the shortest paths. It is calculated as the cumulative of the fractions of the number of shortest paths between the node pairs over how many of them actually pass through the node of interest. It’s possible for a node to have zero or underrated betweenness value while sitting just next to the giant flow of information. These nodes may have a significant influence on the network when the normal flow of information is disrupted. We propose a betweenness centrality measure called collective betweenness that takes into account the surroundings of a node. We will compare our measure with other centrality metrics and show some applications of it.
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