Improved Identification of the function of Novel and Partially Occluded Laboratory Equipment

This STTR Phase II project proposes development of a statistical relational learning framework for identification of the function of laboratory equipment from imagery. The proposed framework uses semantic reasoning to incorporate evidence from multiple classifiers and feature extractors, domain knowledge, and scene context for scene understanding and labeling. The Phase II effort will focus on developing an end-to-end system for laboratory analysis and demonstrating the capabilities for identification of the function of novel and partially occluded laboratory equipment in representative scenarios. The proposed effort will build upon the technologies developed during Phase I of this project and will leverage expertise of the Novateur Team in the areas of target detection and classification, statistical relational learning, deep learning, ontology representations, semantic reasoning for scene understanding and inference, and CBRNE.