Precipitates Segmentation from Scanning Electron Microscope Images through Machine Learning Techniques

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Papa, JP (Author) Other; Pereira, CR (Author) Other; de Albuquerque, VHC (Author) Other; Silva, CC (Author) Other; Falcao, AX (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Conference proceedings International

Title: COMBINATORIAL IMAGE ANALYSIS

Pages: 456-468

14th International Workshop on Combinatorial Image Analysis (IWCIA)

Madrid, SPAIN, MAY 23-25, 2011

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-007-YHP

DOI: 10.1007/978-3-642-21073-0_40

Abstract (EN): The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13