Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO
In the paper, a real-time segmentation method that separates the target signal from the navigation image is proposed. In the approaching docking stage, the navigation image is composed of target and non-target signal, which are separately bright spot and space vehicle itself. Since the non-target si...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 26(2017), 3 vom: 15. März, Seite 1355-1362 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2017
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In the paper, a real-time segmentation method that separates the target signal from the navigation image is proposed. In the approaching docking stage, the navigation image is composed of target and non-target signal, which are separately bright spot and space vehicle itself. Since the non-target signals is the main part of the navigation image, the traditional entropy-related criterions and Ostu-related criterions will bring inadequate segmentation, while the mere 2D Fisher criterion will causes over-segmentation, all the methods show their shortages in dealing with this kind of case. To guarantee a precise image segmentation, a revised 2D fuzzy Fisher is proposed in the paper to make a trade-off between positioning target regions and retaining target fuzzy boundaries. First, to reduce redundant computations in finding the threshold pair, a 2D fuzzy Fisher criterion-based integral image is established by way of simplifying the corresponding fuzzy domains. Then, to quicken the convergence, a random orthogonal component is added in its quasi-optimum particle to enhance its local searching capacity in each iteration. Experimental results show its competence of quick segmentation |
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| Beschreibung: | Date Completed 30.07.2018 Date Revised 30.07.2018 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2016.2621670 |