Challenging illumination conditions (low-light, under-exposure and over-exposure) in the
real world not only cast an unpleasant visual appearance but also taint the computer vision …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Strong noise from complex welding condition such as arc light and splashes lead to high
tracking error in vision-based seam tracking. To solve this problem, this paper proposes an …

Automatic recognition of visual objects using a deep learning approach has been
successfully applied to multiple areas. However, deep learning techniques require a large …

Recent advances of deep neural networks (DNNs) promote low-level vision applications in
real-world scenarios, eg, image enhancement, dehazing. Nevertheless, DNN-based …

Object recognition, commonly performed by a camera, is a fundamental requirement for
robots to complete complex tasks. Some tasks require recognizing objects far from the …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets which are representative of …

This paper uses deep learning to define the grasping points of unknown objects and
proposes an approach to implement for grasping and possible correct picking and placing of …

Nowadays, Generative models are considered as potentially successful tools for various
different tasks with different types of data. Many effective and interesting studies based on …

Human motion prediction is a research field with broad application prospects. With the
development of deep learning, researchers have used advanced deep-learning algorithms …