7 citations · 7 across the 1 of their papers we have counts for
5 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.…
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…
ViewSynth: Learning Local Features from Depth using View Synthesis
Jisan Mahmud, Rajat Vikram Singh, Peri Akiva +3
The rapid development of inexpensive commodity depth sensors has made keypoint detection and matching in the depth image modality an important problem in computer vision. Despite g…