Mean squared error (MSE) is one of the most widely used metrics to expression differences between multi-dimensional entities, including images.
We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability.
Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR).
To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision.
This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles.
Ranked #305 on 3D Human Pose Estimation on Human3.6M
This thesis provides a study of the effects of various factors and hyper-parameters of deep neural networks in the process of determining an optimal network configuration for the task of semantic facial feature recognition.