The following publications are possibly variants of this publication:
- An accurate estimation of preterm infants' limb pose from depth images using deep neural networks with densely connected atrous spatial convolutionsLucia Migliorelli, Emanuele Frontoni, Sara Moccia. eswa, 204:117458, 2022. [doi]
- End-to-end semantic joint detection and limb-pose estimation from depth images of preterm infants in NICUsMatteo Carbonari, Greta Vallasciani, Lucia Migliorelli, Emanuele Frontoni, Sara Moccia. ISCC 2021: 1-6 [doi]
- Improving Preterm Infants' Joint Detection in Depth Images Via Dense Convolutional Neural NetworksLucia Migliorelli, Emanuele Frontoni, Simone Appugliese, Giuseppe Pio Cannata, Virginio Paolo Carnielli, Sara Moccia. embc 2021: 3013-3016 [doi]
- Generating depth images of preterm infants in given poses using GANsGiuseppe Pio Cannata, Lucia Migliorelli, Adriano Mancini, Emanuele Frontoni, Rocco Pietrini, Sara Moccia. cmpb, 225:107057, 2022. [doi]
- TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth imagesLucia Migliorelli, Alessandro Cacciatore, Valeria Ottaviani, Daniele Berardini, Raffaele L. DellacĂ , Emanuele Frontoni, Sara Moccia. mbec, 61(2):387-397, February 2023. [doi]
- Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose EstimationLucia Migliorelli, Daniele Berardini, Francesca Rossini, Emanuele Frontoni, Virginio Paolo Carnielli, Sara Moccia. embc 2021: 3021-3024 [doi]