20 citations · 39 across the 7 of their papers we have counts for
8 papers · 1 filter
ISETHDR: A Physics-based Synthetic Radiance Dataset for High Dynamic Range Driving Scenes
Zhenyi Liu, Devesh Shah, Brian Wandell
This paper describes a physics-based end-to-end software simulation for image systems. We use the software to explore sensors designed to enhance performance in high dynamic range…
Using simulation to quantify the performance of automotive perception systems
Zhenyi Liu, Devesh Shah, Alireza Rahimpour +3
The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance…
ISETAuto: Detecting vehicles with depth and radiance information
Zhenyi Liu, Joyce Farrell, Brian Wandell
Autonomous driving applications use two types of sensor systems to identify vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the performance (average precisi…
Neural Network Generalization: The impact of camera parameters
Zhenyi Liu, Trisha Lian, Joyce Farrell +1
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 data…
A convolutional neural network reaches optimal sensitivity for detecting some, but not all, patterns
Fabian H. Reith, Brian A. Wandell
We investigate the performance of modern convolutional neural networks (CNN) and a linear support vector machine (SVM) with respect to spatial contrast sensitivity. Specifically, w…
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network
Zhenyi Liu, Trisha Lian, Joyce Farrell +1
Imaging systems are increasingly used as input to convolutional neural networks (CNN) for object detection; we would like to design cameras that are optimized for this purpose. It…