Tag: deep convolutional neural networks

  Dr. Alina Zare recently presented in the University of Florida Informatics Institute’s virtual seminar on AI Advances and Applications. During her talk, Alina discussed how the Machine Learning and Sensing Lab is using AI methods to advance the understanding of plant root systems.   Check out our Publications page for more info on the exciting […]

Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently published to Applications in Plant Sciences (APPS). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

Congratulations to our labmates, Matt Cook, Alina Zare and Paul Gader for presenting at the 37th International Conference on Machine Learning (ICML) Workshop on Uncertainty and Robustness in Machine Learning! Their paper, titled “Outlier Detection through Null Space Analysis of Neural Networks”, introduces a novel method for detecting outliers in a set of data. Matt will […]

Abstract: Many machine learning classification systems lack competency awareness. Specifically, many systems lack the ability to identify when outliers (e.g., samples that are distinct from and not represented in the training data distribution) are being presented to the system. The ability to detect outliers is of practical significance since it can help the system behave […]

Congratulations to our labmates, Weihuang Xu, Guohao Yu and Alina Zare, as well as collaborators Brenden Zurweller, Diane Rowland, Joel Reyes-Cabrera, Felix Fritschi, Roser Matamala and Thomas Juenger!  Their paper, “Overcoming Small Minirhizotron Datasets Using Transfer Learning”, was published in Computers and Electronics in Agriculture.   The document and code can be found here.  Make sure […]

Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently accepted to Applications in Plant Sciences (APP). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

Abstract: High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is […]

Congratulations to our labmates, Hudanyun Sheng and Princess Lyons, for successful Master’s defenses!   Hudanyun conducted work on “Switchgrass Genotype Classification using Hyperspectral Imagery”, while Princess investigated  “Anomaly and Target Detection in Synthetic Aperture Sonar”. Great job, you two!

Congratulations to our labmate Guahao Yu 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.  Guohao is planning to advance image segmentation techniques using artificial neural networks trained with weak annotations.   We are excited to see where your […]

Abstract: In underwater synthetic aperture sonar (SAS) imagery, there is a need for accurate target recognition algorithms. Automated detection of underwater objects has many applications, not the least of which being the safe extraction of dangerous explosives. In this paper, we discuss experiments on a deep learning approach to binary classification of target and non-target […]