HIGH PRECISION AUTOMATIC EXTRACTION OF CULTURAL RELIC DISEASES BASED ON IMPROVED SLIC AND AP CLUSTERING
Abstract. Automatic and high-precision detection and quantitative expression of cultural relics diseases are important contents of cultural relics science and technology protection. Aiming at the problem of automatic extraction of boundary cultural relics diseases, this paper proposes an adaptive SLIC0 combined with AP clustering method to achieve high-precision detection of disease areas. Firstly, based on the SLIC0 algorithm, the selected area of the disease orthophoto frame is segmented, and the Canny edge detection is used as the true value. The number of superpixels is iterative until the accuracy meets the requirements, so as to achieve the best fitting of superpixel edges. Then the AP clustering method is used to merge the superpixels of the disease area to obtain the edge information. Finally, taking the surface shedding disease of painted cultural relics as an example, this method is applied to realize the high precision extraction and quantitative expression of the edge of the disease. The correctness, feasibility and advancement of the algorithm are proved by comparing with the existing manual methods. The method in this paper provides an efficient and high-precision means for the quantitative expression of cultural relics diseases, and can provide accurate data support for the scientific and technological restoration of cultural relics.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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HIGH PRECISION AUTOMATIC EXTRACTION OF CULTURAL RELIC DISEASES BASED ON IMPROVED SLIC AND AP CLUSTERING ; volume:XLIII-B2-2022 ; year:2022 ; pages:801-807 ; extent:7
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLIII-B2-2022 (2022), 801-807 (gesamt 7)
- Urheber
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Hu, C.
Huang, X.
Xia, G.
Wang, Y.
Liu, X.
Ma, X.
- DOI
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10.5194/isprs-archives-XLIII-B2-2022-801-2022
- URN
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urn:nbn:de:101:1-2022060206132514660846
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:29 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Hu, C.
- Huang, X.
- Xia, G.
- Wang, Y.
- Liu, X.
- Ma, X.
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