most citedLow-Latency Scalable Streaming for Event-Based Vision

2 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.MM2025

adder-viz: Real-Time Visualization Software for Transcoding Event Video

Andrew C. Freeman, Luke Reinkensmeyer

Recent years have brought about a surge in neuromorphic ``event'' video research, primarily targeting computer vision applications. Event video eschews video frames in favor of asy…

eess.IV20251 cited

Scalable Event-Based Video Streaming for Machines with MoQ

Andrew C. Freeman

Lossy compression and rate-adaptive streaming are a mainstay in traditional video steams. However, a new class of neuromorphic ``event'' sensors records video with asynchronous pix…

cs.CV2025

Efficient Multi-Crop Saliency Partitioning for Automatic Image Cropping

Andrew Hamara, Andrew C. Freeman

Automatic image cropping aims to extract the most visually salient regions while preserving essential composition elements. Traditional saliency-aware cropping methods optimize a s…

cs.CV2025

Learning to Plan via Supervised Contrastive Learning and Strategic Interpolation: A Chess Case Study

Andrew Hamara, Greg Hamerly, Pablo Rivas +1

Modern chess engines achieve superhuman performance through deep tree search and regressive evaluation, while human players rely on intuition to select candidate moves followed by…

cs.CV20242 cited

Low-Latency Scalable Streaming for Event-Based Vision

Andrew Hamara, Benjamin Kilpatrick, Alex Baratta +2

Recently, we have witnessed the rise of novel ``event-based'' camera sensors for high-speed, low-power video capture. Rather than recording discrete image frames, these sensors out…

cs.MM2024

An Open Software Suite for Event-Based Video

Andrew C. Freeman

While traditional video representations are organized around discrete image frames, event-based video is a new paradigm that forgoes image frames altogether. Rather, pixel samples…