In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Abstract Graph Convolutional Neural Networks (graph CNNs) have been widely used for
graph data representation and semi-supervised learning tasks. However, existing graph …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

Patch based image modeling has achieved a great success in low level vision such as
image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which …

Inspired by the great success of deep neural networks (DNNs), many unfolding methods
have been proposed to integrate traditional image modeling techniques, such as dictionary …

Cognitive computing is dedicated to researching a computing principle and method that can
simulate the intelligence ability of human brain. Human emotion is the basic component of …

Multi-focus image fusion has emerged as a major topic in image processing to generate all-
focus images with increased depth-of-field from multi-focus photographs. Different …

The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …

Human actions are often captured by multiple cameras (or sensors) to overcome the
significant variations in viewpoints, background clutter, object speed, and motion patterns in …