Assessing the Impact of the Deceived Non Local Means Filter as a Preprocessing Stage in a Convolutional Neural Network Based Approach for Age Estimation Using Digital Hand X-Ray Images

S. Calderon, F. Fallas, M. Zumbado, P. N. Tyrrell, H. Stark, Z. Emersic, B. Meden, M. Solis

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

20 Citas (Scopus)

Resumen

In this work we analyze the impact of denoising, contrast and edge enhancement using the Deceived Non Local Means (DNLM) filter in a Convolutional Neural Network (CNN) based approach for age estimation using digital X-ray images from hands. The DNLM filter presents two parameters which control edge enhancement and denoising. Increasing levels were tested to assess the impact of both contrast enhancement and denoising in the CNN based model regression accuracy. Results obtained showed that contrast enhancement was important for preprocessing in a CNN based approach, given a statistically significant 42% lower root mean squared error, with comparable to previous state of the art results, using larger publicly available dataset. The obtained results suggest that both image enhancement and denoising can significantly improve results in a CNN based model.

Idioma originalInglés
Título de la publicación alojada2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
EditorialIEEE Computer Society
Páginas1752-1756
Número de páginas5
ISBN (versión digital)9781479970612
DOI
EstadoPublicada - 29 ago 2018
Evento25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Grecia
Duración: 7 oct 201810 oct 2018

Serie de la publicación

NombreProceedings - International Conference on Image Processing, ICIP
ISSN (versión impresa)1522-4880

Conferencia

Conferencia25th IEEE International Conference on Image Processing, ICIP 2018
País/TerritorioGrecia
CiudadAthens
Período7/10/1810/10/18

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