259 citations
- General Electric (United States)US11 papers
- University at Albany, State University of New YorkUS5 papers
- University of Maryland, College ParkUS4 papers
- Rensselaer Polytechnic InstituteUS3 papers
- California Institute of TechnologyUS2 papers
- Eindhoven University of TechnologyNL2 papers
- Pennsylvania State UniversityUS2 papers
- Purdue University West LafayetteUS2 papers
- Technion – Israel Institute of TechnologyIL2 papers
- Tel Aviv UniversityIL2 papers
- University College LondonGB2 papers
- University of California, BerkeleyUS2 papers
15 papers · 1 filter
Artificial Intelligence in PET: an Industry Perspective
Arkadiusz Sitek, Sangtae Ahn, Evren Asma +6
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has t…
Emergent symbolic language based deep medical image classification
Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht +1
Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they ar…
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation
Xia Li, Yen-Liang Lin, James Miller +2
As we begin to consider modeling large, realistic 3D building scenes, it becomes necessary to consider a more compact representation over the polygonal mesh model. Due to the large…
Learning Non-Uniform Hypergraph for Multi-Object Tracking
Longyin Wen, Dawei Du, Shengkun Li +2
The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them l…
Evolvement Constrained Adversarial Learning for Video Style Transfer
Wenbo Li, Longyin Wen, Xiao Bian +1
Video style transfer is a useful component for applications such as augmented reality, non-photorealistic rendering, and interactive games. Many existing methods use optical flow t…
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
Shifeng Zhang, Longyin Wen, Xiao Bian +2
Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other. In this paper, we propose a new occlusion-aware…