most citedDrive-Net: Convolutional Network for Driver Distraction Detection

59 citations · 88 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2020

Object sieving and morphological closing to reduce false detections in wide-area aerial imagery

Xin Gao, Sundaresh Ram, Jeffrey J. Rodriguez

For object detection in wide-area aerial imagery, post-processing is usually needed to reduce false detections. We propose a two-stage post-processing scheme which comprises an are…

eess.IV20206 cited

Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

Xin Gao, Jeno Szep, Pratik Satam +3

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicl…

cs.CV202059 cited

Drive-Net: Convolutional Network for Driver Distraction Detection

Mohammed S. Majdi, Sundaresh Ram, Jonathan T. Gill +1

To help prevent motor vehicle accidents, there has been significant interest in finding an automated method to recognize signs of driver distraction, such as talking to passengers,…

eess.IV202019 cited

Deep learning classification of chest x-ray images

Mohammad S. Majdi, Khalil N. Salman, Michael F. Morris +2

We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provide…

eess.IV2019

Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural Networks

Mohammad S Majdi, Mahesh B Keerthivasan, Brian K Rutt +3

A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared…

eess.IV20194 cited

A Conditional Random Field Model for Context Aware Cloud Detection in Sky Images

Vijai T. Jayadevan, Jeffrey J. Rodriguez, Alexander D. Cronin

A conditional random field (CRF) model for cloud detection in ground based sky images is presented. We show that very high cloud detection accuracy can be achieved by combining a d…