| Autor: |
Espinosa-Bernal, Osmar Antonio, Pedraza-Ortega, Jesús Carlos, Aceves-Fernandez, Marco Antonio, Ramos-Arreguín, Juan Manuel, Tovar-Arriaga, Saul, Gorrostieta-Hurtado, Efrén |
| Předmět: |
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| Zdroj: |
Computers (2073-431X); Nov2024, Vol. 13 Issue 11, p290, 19p |
| Abstrakt: |
Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. However, there is a problem that is inherent to this technique, and that is the quasi-periodic noise that appears due to this technique and considerably affects the final 3D object reconstructed. Many studies have been carried out to tackle this problem to obtain a 3D object close to the original one. The application of deep learning in many areas of research presents a great opportunity to to reduce or eliminate the quasi-periodic noise that affects images. Therefore, a model of convolutional neural network along with four different patterns of frequencies projected in the three-step technique is researched in this work. The inferences produced by models trained with different frequencies are compared with the original ones both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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