The proposed work focused on a methodological approach to check defects by adopting the Watershed approach. The adopted image segmentation technique was applied by estimating the defect extent of manufactured pieces. In particular, the experimental tests were performed by characterizing defected holes and defining tolerance gaps on the performed measurements. Information about image segmentation sensitivity was provided by varying camera distances from the sample. Additionally, the defect identification was proved by applying the K-Means algorithm as a processing tool on the analyzed image. The work was developed within a research industrial project.

Image Processing Segmentation Applied on Defect Estimation in Production Processes

Massaro A;
2020-01-01

Abstract

The proposed work focused on a methodological approach to check defects by adopting the Watershed approach. The adopted image segmentation technique was applied by estimating the defect extent of manufactured pieces. In particular, the experimental tests were performed by characterizing defected holes and defining tolerance gaps on the performed measurements. Information about image segmentation sensitivity was provided by varying camera distances from the sample. Additionally, the defect identification was proved by applying the K-Means algorithm as a processing tool on the analyzed image. The work was developed within a research industrial project.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12572/18339
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact