Deep Learning on a Raspberry Pi for Real Time Face Recognition

Real-time Standard View Classification in Transthoracic Echocardiography using Convolutional Neural Networks.. Andreas Østvik a , Erik Smistad a,b , Svein Arne Aase c , Bjørn

Echogram with four frequency channels (a–d, 18, 38, 120 and 200 kHz) and original (e) and modified (f) annotations, where black is the “background” class, red (grey in print) is

Keywords: deep learning; Bayesian convolutional neural network; uncertainty quantification; time series classification; CO 2 -leak

An original face recognition approach based on 2D and 3D Radial Geodesic Distances (RGDs), respectively computed on 2D face images and 3D face models, is proposed in this work1. In

The proposed system processes each image of an individual separately, but we also present a new approach that is based on combining several decisions into a single meta-decision

by augmenting the training data In conclusion our approach leads to signifcant improvements with respect to both speed and accuracy. ,

As the question of efficiently using deep Convolutional Neural Networks (CNNs) on 3D data is still a pending issue, we propose a framework which applies CNNs on multiple 2D image

With ongoing progress in artificial intelligence and neural networks, recent works present deep learning based approaches for motion synthesis, which offer great potential for