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N°376 – Continuous spike and waves during slow sleep in the neurological department of Fann National University hospital of Dakar: Topographic aspects and age

2023; Elsevier BV; Volume: 150; Linguagem: Inglês

10.1016/j.clinph.2023.03.347

1872-8952

Lala Bouna Seck, Mamadou Moustapha Sarr, H. Akrim, Mamadou Mourtalla Ka, Alassane Mamadou Diop, Marième Soda Sene, Arame Gaye, Anna Modji Basse Faye, Adjaratou Djeynabou Sow, Moustapha Ndiaye, Amadou Diop,

Visual perception and processing mechanisms

The automotive augmented reality head-up-display (AR-HUD) system relies on multiple free-form surfaces to project the virtual image into human eyes. A wide range of views and the extensive display area result in extraordinarily complex distortions. Providing a way to correct such distortions is a major leap forward. Methods widely used for calibration, such as lookup tables and interpolation, require considerable memory capacity, complicated calibration procedures, etc. Additionally, when attempting to meet the high-accuracy requirements for such systems, computational memory grow rapidly. In this context, we propose a fully connected neural network (HUDNet) for an automotive AR-HUD to correct dynamic distortion. Here, we propose a parallel prediction framework for the center and the edge of the image, and separate the distortion information from the disparity information in order to balance the unstable central field of view error. Ultimately, we introduce the transfer learning method to improve accuracy and its effect on accuracy is assessed. The present study provides a reference for us to apply deep learning methods to predict dynamic image distortion of various complex multi-freeform surface reflection systems in the future.