Abstract: Objective: We quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact Statement: To our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue… Read More
NewsNews
Celebrating Dr. Guohao Yu’s achievement
It is always a momentous occasion someone you know accomplishes what they have been working years for. Gatorsense is proud of Dr. Guohao Yu, who recently defended his PhD thesis! His work and dedication is an inspiration for the current… Read More
Congratulations to Dr. Guohao Yu, our lab’s latest PhD graduate!
It is a great pleasure and honor for everyone in Gatorsense that one of our labmates has achieved his goal. Congratulations to Dr. Guohao Yu for graduating with his Ph.D.! Guohao’s dissertation is titled “Weakly Supervised Image Segmentation with Multiple Instance… Read More
RANDCROWNS accepted to IEEE JSTARS, 2021!
Congratulations to our labmates and collaborators: Dylan Stewart, Alina Zare, Sergio Marconi, Ben Weinstein, Ethan White, Sarah Grave, Stephanie Bohlman and Aditya Singh! Their paper, “RANDCROWNS: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation”, was recently accepted to IEEE… Read More
Congratulations to Yiming Cui for a Successful Proposal Defense!
Congratulations to our labmate Yiming Cui for successfully defending his research proposal! Defending an oral research proposal is the second of four milestones to completing a Ph.D. at the University of Florida. Yiming is planning to conduct point cloud semantic… Read More
Welcome New Undergraduate Research Assistant, Daniel Shmul!
We are pleased to welcome Daniel Shmul as one of our new undergraduate research assistants in 2021! Daniel majors in Computer engineering in the UF Department of Electrical & Computer Engineering. He’s part of the AI MESH team where he’s… Read More
Robust Semi-Supervised Classification using GANs with Self-Organizing Maps
Abstract: Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification. However, to this point, semi-supervised GAN methods make the assumption that the unlabeled data set contains only samples of the… Read More
Lace for image classification accepted to WACV 2022!
Congratulations to our labmates: Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart and Alina Zare! Their paper, “LEARNABLE ADAPTIVE COSINE ESTIMATOR (LACE) FOR IMAGE CLASSIFICATION “, was recently accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022.… Read More
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Abstract: In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator [42] (ACE), our proposed method incorporates angular information that is inherently learned by artificial neural networks. Our learnable… Read More
Spatial and Texture Analysis of Root System Architecture with Earth Mover’s Distance (STARSEED)
Abstract: Purpose: Root system architectures are complex, multidimensional, and challenging to characterize effectively for agronomic and ecological discovery. Methods: We propose a new method, Spatial and Texture Analysis of Root System architecture with Earth mover’s Distance (STARSEED), for comparing root… Read More