In this paper we evaluate the combination of hand-crafted and deep learning-based features for neonatal pain assessment. To this end we consider two hand-crafted descriptors, i.e. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG), and features extracted from two pre-trained Convolutional Neural Networks (CNNs). Experimental results on the publicly available Infant Classification Of Pain Expressions (COPE) database show competitive results compared to previous methods.
Celona, L., & Manoni, L. (2017). Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features. In New Trends in Image Analysis and Processing – ICIAP 2017 (pp.197-204). Springer Verlag [10.1007/978-3-319-70742-6_19].
Citazione: | Celona, L., & Manoni, L. (2017). Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features. In New Trends in Image Analysis and Processing – ICIAP 2017 (pp.197-204). Springer Verlag [10.1007/978-3-319-70742-6_19]. | |
Tipo: | paper | |
Carattere della pubblicazione: | Scientifica | |
Presenza di un coautore afferente ad Istituzioni straniere: | No | |
Titolo: | Neonatal Facial Pain Assessment Combining Hand-Crafted and Deep Features | |
Autori: | Celona, L; Manoni, L | |
Autori: | ||
Data di pubblicazione: | 2017 | |
Lingua: | English | |
Nome del convegno: | International Conference on Image Analysis and Processing, Automatic affect analysis and synthesis - 3AS 2017 11-15 September | |
ISBN: | 9783319707419 | |
Serie: | LECTURE NOTES IN COMPUTER SCIENCE | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-319-70742-6_19 | |
Appare nelle tipologie: | 02 - Intervento a convegno |