Name
Title | ||
|---|---|---|
Uncertain active contour model based on rough and fuzzy sets for auroral oval segmentation. |
Abstract | ||
|---|---|---|
The shape of the earth's aurora serves as a direct monitor of important physical processes in the magnetosphere. Detecting auroral regions is therefore a crucial step in studying auroral activity in the field of space physics. Since auroral ovals are captured as images under undesirable conditions of low illumination, intensity distributions overlapping between auroral oval regions and the background present challenges for auroral oval segmentation methods. Granular computing, including fuzzy sets and rough sets, is an appropriate choice to better handle the uncertainty inherent in observed images. In this paper, we present a novel active contour model unaffected by intensity inhomogeneity for detecting auroral ovals in satellite imagery. By integrating the principles of fuzzy sets and rough sets, we develop a technique to automatically detect class boundaries. The resulting characterization leads to an efficient description of uncertain regions near auroral oval boundaries, as well as uncertainty in class boundaries. The approach by which an image is approximated by regions with piecewise-constant intensities within different local regions is more suitable for auroral images with intensity inhomogeneity. Considering local image information, we therefore use spatially varied thresholding for each pixel rather than constant thresholding. As a result, our proposed method can effectively and efficiently segment auroral ovals whose boundaries are not easily separated from the background. Further, experimental results on auroral oval images demonstrate the effectiveness of our proposed method in terms of human visual perception and segmentation accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.04.017 | Information Sciences |
Keywords | Field | DocType |
Fuzzy sets,Rough sets,Auroral oval segmentation,Local image information,Active contour model | Active contour model,Pattern recognition,Segmentation,Fuzzy set,Rough set,Space physics,Granular computing,Artificial intelligence,Pixel,Thresholding,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
492 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiao Shi | 1 | 151 | 9.85 |
| Yu Lei | 2 | 7 | 5.92 |
| Jiaji Wu | 3 | 137 | 22.60 |
| Gwanggil Jeon | 4 | 596 | 117.99 |