We present an approach to enhancing the realism of synthetic images. The images are
enhanced by a convolutional network that leverages intermediate representations produced …

Data science as a flourishing interdisciplinary domain of computer and mathematical
sciences is playing an important role in guiding the porous material research streams. In the …

Sensor models are essential for virtual testing of Advanced Driver Assistance
Systems/Autonomous Driving (ADAS/AD) functions. This article gives an overview of the …

We quantify the generalization of a convolutional neural network (CNN) trained to identify
cars. First, we perform a series of experiments to train the network using one image dataset …

Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually
have a double-loop training process, alternating between learning a reward function and a …

Due to the impressive performance of deep neural networks (DNNs) for visual perception,
there is an increased demand for their use in automated systems. However, to use deep …

Camera image simulation is integral to the virtual validation of autonomous vehicles and
robots that use visual perception to understand their environment. It also has applications in …

Abstract To train Deep Neural Networks (DNNs)-based methods, suitable training data are
key to help DNNs learn appropriate pattern recognition features. The use of synthetic data …

Deep regression trackers are among the fastest tracking algorithms available, and therefore
suitable for real-time robotic applications. However, their accuracy is inadequate in many …

Perception sensors, particularly camera and Lidar, are key elements of Autonomous Driving
Systems (ADS) that enable them to comprehend their surroundings for informed driving and …