Since their introduction as a means of front propagation and their first application to edge-
based segmentation in the early 90's, level set methods have become increasingly popular …

A brain–computer interface (BCI) enables a user to communicate directly with an external
device, eg, a computer, using brain signals. It can be used to research, map, assist …

We propose a deep bilinear model for blind image quality assessment that works for both
synthetically and authentically distorted images. Our model constitutes two streams of deep …

Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI)
decoding is currently attracting increasing attention, due to accumulating documentation of …

Describing the color and textural information of a person image is one of the most crucial
aspects of person re-identification. In this paper, we present a novel descriptor based on a …

Functional connectomes reveal biomarkers of individual psychological or clinical traits.
However, there is great variability in the analytic pipelines typically used to derive them from …

Objective: This paper tackles the problem of transfer learning in the context of
electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In …

Abstract Symmetric Positive Definite (SPD) matrix learning methods have become popular in
many image and video processing tasks, thanks to their ability to learn appropriate statistical …

A major goal of neuroimaging studies is to develop predictive models to analyze the
relationship between whole brain functional connectivity patterns and behavioural traits …

By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets)
effectively learn from low-level to high-level features and discriminative representations …