Professor Henry Medeiros receives prestigious NSF CAREER award: "A Unifying Stochastic Framework for Temporally Consistent Computer Vision Models."
The $515,000 five-year award from the National Science Foundation enables the design of autonomous and robotic systems that can interpret and interact with their environment. Dr. Medeiros' novel concept combines sequential Monte Carlo methods with neural networks to create a trainable stochastic framework for computer vision tasks. In collaboration with USDA scientists, the methods generated in this project will be demonstrated through the development of novel agricultural robotic systems that generate accurate models of agricultural crops at varying levels of spatial and temporal granularity.
Beyond the current application, the research will create a general stochastic framework that learns in an end-to-end manner how to leverage semantic information about objects of interest to assimilate spatial and temporal visual information and enforce temporal consistency in computer vision algorithms.