8 citations · 20 across the 5 of their papers we have counts for
13 papers
Towards Single Stage Weakly Supervised Semantic Segmentation
Peri Akiva, Kristin Dana
The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or b…
AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk
Peri Akiva, Benjamin Planche, Aditi Roy +3
Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-to-end cranberry health monitorin…
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement
Peri Akiva, Matthew Purri, Kristin Dana +2
Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information.…
Differential Viewpoints for Ground Terrain Material Recognition
Jia Xue, Hang Zhang, Ko Nishino +1
Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based repres…
Angular Luminance for Material Segmentation
Jia Xue, Matthew Purri, Kristin Dana
Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. Wit…
Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors
Peri Akiva, Kristin Dana, Peter Oudemans +1
Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers. In this work, we present a deep learning method for…