Simulation is increasingly important in the development and testing of robots and
autonomous vehicles as it opens the door for candidate navigation, perception, and sensor …

Image synthesis designed for machine learning applications provides the means to
efficiently generate large quantities of training data while controlling the generation process …

To advance research in learning-based defogging algorithms various synthetic fog datasets
have been developed. However exsiting datasets created using the Atmospheric Scattering …

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 …

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 …

Image systems simulation software can accelerate innovation by reducing many of the time-
consuming and expensive steps in designing, building, and evaluating image systems. To …

Autonomous driving applications use two types of sensor systems to detect vehicles-depth
sensing LiDAR and radiance sensing cameras. We compare the performance (average …

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 …

In the era of artificial intelligence perceptual algorithms used in state-of-the-art Advanced
Driver Assistance Systems (ADAS), algorithm validation is not an easy task. To ensure the …

The performance of perception tasks is heavily influenced by imaging systems. However,
designing cameras with high task performance is costly, requiring extensive camera …