New computational solution to quantify synthetic material porosity from optical...

• Autor(es): V.H.C. de Albuquerque ; P.P. Rebouças Filho ; T.S. Cavalcante ; J.M.R.S. Tavares
• Data: 2010
• Identificador Persistente: http://hdl.handle.net/10216/25379
• Origem: Repositório Aberto da Universidade do Porto
• Assunto(s): Ciências tecnológicas
• Tipo de Documento: Artigo
• Idioma: Inglês

Descrição

This paper presents a new computational solution to quantify the porosity of synthetic materials from optical microscopic images. The solution is based on an artificial neuronal network of the multilayer perceptron type and a backpropagation algorithm is used for training. To evaluate this new solution, 40 sample images of a synthetic material were analyzed and the quality of the results was confirmed by human visual analysis. Additionally, these results were compared with ones obtained with a commonly used commercial system confirming their superior quality and the shorter time needed. The effect of images with noise was also studied and the new solution showed itself to be more reliable. The training phase of the new solution was analyzed confirming that it can be performed in a very easy and straightforward manner. Thus, the new solution demonstrated that it is a valid and adequate option for researchers, engineers, specialists and other professionals to quantify the porosity of materials from microscopic images in an automatic, fast, efficient and reliable manner.