| Texto completo | |
| Autor(es): |
Sa, Jarbas Joaci de Mesquita, Jr.
;
Backes, Andre R.
;
Bruno, Odemir Martinez
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2017; v. 10657, p. 8-pg., 2018-01-01. |
| Resumo | |
This paper presents the application of the randomized neural network based signature, an innovative and powerful texture analysis algorithm, to a relevant problem of metallography, which consists of classifying zones of titanium alloys Ti-6Al-4V into two categories: "alpha and beta" and "alpha+ beta". The obtained results are very promising, with accuracy of 98.84% by using LDA, and accuracy of 98.64%, precision of 99.11% for "alpha and beta", and precision of 98.09% for "alpha+ beta" by using SVM. This performance suggests that this texture analysis method is a valuable tool that can be applied to many other problems of metallography. (AU) | |
| Processo FAPESP: | 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal |
| Beneficiário: | Odemir Martinez Bruno |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |