New computational solution to quantify synthetic material porosity from optical microscopic images

• Type: Article in International Scientific Journal
• Date: 2010

Authors

V. H. C. de Albuquerque (Author) FEUP; P. P. Rebouças Filho (Author) Other; T. S. Cavalcante (Author) Other; J. M. R. S. Tavares (Author) FEUP

Journal

Title: Transactions of the Microscopical Society of London

Vol. 240 No. 1

Pages: 50-59

ISSN: 0022-2720

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 14 Citations

ISI Web of Science

Scopus - 20 Citations

INSPEC

Scientific classification

FOS: Engineering and technology > Other engineering and technologies

CORDIS: Technological sciences

Other information

Authenticus ID: P-003-2GY

DOI: 10.1111/j.1365-2818.2010.03384.x

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific