Building Material Recognition and Feature Extraction Using Small CCD Sensor and Image Analysis and Clustering Techniques

• Autorzy: Pagnotta Stefano , Filimon Danis Ionut , Gallello Gianni , Lezzerini Marco

Identyfikatory

DOI

10.29227/IM-2024-01-25

Warianty tytułu

PL

Rozpoznawanie materiałów budowlanych i ekstrakcja cech przy użyciu małego czujnika CCD oraz technik analizy obrazu i klastrowania

• Języki publikacji: EN

Abstrakty

EN

Heritage Building material recognition is the process of classifying building materials based on their visual appearance. It is important in construction, urban planning, and archaeology. Image analysis is a common approach, starting with acquiring RGB images, then extracting features using techniques such as colour histograms and texture analysis, and clustering the materials into groups using algorithms like k-means. Finally, the materials are classified into categories using classifiers like decision trees, SVM, or neural networks. Image analysis is a useful tool for building material recognition, as it allows for accurate classification of building materials based on their visual characteristics.

Słowa kluczowe

EN

material recognition; building material; feature extraction; ccd sensor; image analysis; clustering techniques

PL

materiał; rozpoznawanie; materiał budowlany; ekstrakcja cech; czujnik CCD; analiza obrazu; techniki grupowania

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2024
• Tom: Vol. 1, no. 1
• Strony: 229--236
• Opis fizyczny: Bibliogr. 24 poz., rys., zdj.

Twórcy

autor

Pagnotta Stefano

• Department of Earth Sciences, University of Pisa, Via S. Maria 53 - 56126, Pisa, Italy

• https://orcid.org/0000-0002-6645-4924

autor

Filimon Danis Ionut

• Department of Earth Sciences, University of Pisa, Via S. Maria 53 - 56126, Pisa, Italy

autor

Gallello Gianni

• Department of Prehistory, Archeology and Ancient History University of Valencia Av. Blasco Ibáñez 28 - 46010, Valencia, Spain

• https://orcid.org/0000-0003-3641-8815

autor

Lezzerini Marco

• Department of Earth Sciences, University of Pisa, Via S. Maria 53 - 56126, Pisa, Italy

• https://orcid.org/0000-0001-9204-4462

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).