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RANDCROWNS accepted to IEEE JSTARS, 2021!

November 19, 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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. In the paper, […]

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Congratulations to Yiming Cui for a Successful Proposal Defense!

October 30, 2021

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 segmentation techniques using graph convolutional networks trained with weak annotations. We are excited to see […]

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Welcome New Undergraduate Research Assistant, Daniel Shmul!

October 30, 2021

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 working on data entry and analysis of crop yields. His hobbies include traveling, being in […]

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Robust Semi-Supervised Classification using GANs with Self-Organizing Maps

October 21, 2021

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 joint distribution of the classes of interest, referred to as inliers. Consequently, when presented with […]

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Lace for image classification accepted to WACV 2022!

October 12, 2021

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. In the paper, the authors propose a new loss to improve feature discriminability and classification […]

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Learnable Adaptive Cosine Estimator (LACE) for Image Classification

October 12, 2021

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 ACE (LACE) transforms the data into a new “whitened” space that improves the inter-class separability […]

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Spatial and Texture Analysis of Root System Architecture with Earth Mover’s Distance (STARSEED)

October 8, 2021

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 architectures that incorporate spatial information through a novel application of the Earth Mover’s Distance (EMD). […]

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Welcome New Undergraduate Research Assistant, Brian Hicks!

September 8, 2021

We are pleased to welcome Brian Hicks as one of our new undergraduate research assistants in 2021! Brian majors in Computer Science in the UF Department of Computer & Information Science & Engineering. Brian works on segmentation of Blueberry root images. The goal is to apply a model pre-trained on Peanut root images to Blueberry […]

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Welcome New Undergraduate Research Assistant, Matthew Schrank!

September 3, 2021

We are pleased to welcome Matthew Schrank as one of our new undergraduate research assistants in 2021! Matthew majors in Computer Science in the UF Department of Computer & Information Science & Engineering. He joins us the UF team of 9-university project that aims at developing switchgrass strains, which can be converted to biofuel while […]

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FDI Correction Physics-Based Model accepted to Applied Sciences!

September 1, 2021

Congratulations to our labmates and collaborators: Tierui Zou, Nader Aljohani, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare and Janise McNair! Their paper, “A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach “, was recently accepted to Applied Sciences. In the paper, the authors present an […]

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