30 citations · 67 across the 6 of their papers we have counts for
6 papers
FathomVerse: A community science dataset for ocean animal discovery
Genevieve Patterson, Joost Daniels, Benjamin Woodward +4
Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encoun…
The FathomNet2023 Competition Dataset
Eric Orenstein, Kevin Barnard, Lonny Lundsten +3
Ocean scientists have been collecting visual data to study marine organisms for decades. These images and videos are extremely valuable both for basic science and environmental mon…
Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation
Alison Crosby, Eric C. Orenstein, Susan E. Poulton +5
Ocean scientists studying diverse organisms and phenomena increasingly rely on imaging devices for their research. These scientists have many tools to collect their data, but few r…
FathomNet: A global image database for enabling artificial intelligence in the ocean
Kakani Katija, Eric Orenstein, Brian Schlining +8
The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As…
FathomNet: An underwater image training database for ocean exploration and discovery
Océane Boulais, Ben Woodward, Brian Schlining +4
Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets. However, current manual methods of analysis imped…
Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
Onur Ozdemir, Benjamin Woodward, Andrew A. Berlin
Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convol…