Automated object tracking, event detection, and recognition for high‐speed video of drop formation phenomena

Abstract

Optical imaging technologies have the potential to provide detailed information which can inform process design decisions via modeling of critical phenomena or provide innovative process sensors for use in online monitoring and control strategies. In this work, novel algorithms are developed for automated object tracking, event detection, and classification in high‐speed imaging sequences of drop breakup and coalescence. Using generalization of the physical patterns, a combined strategy extracts quantitative information about the spatiotemporal trajectory and events of every fluid‐object, mapping its lineage and relations to other objects without the need for a training dataset. This high‐level reference frame makes the extracted information easily accessible for evaluation of quantities over sets of objects or events of interest. Included results demonstrate the capability of the software for handling complex multievent scenarios at acceptable execution times, processing 2.7 GB image data (22,000 images/30 videos) in 110–115 s in MATLAB on a standard desktop computer.

Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations

“…The advancements in both optical imaging and machine learning (ML) in the past decade enabled detailed process design and optimization of complex systems with data and measurements that were inaccessible before. , CV is a technology suitable for the acquisition, processing, and analysis of visual inputs (e.g., digital images/videos) and, therefore, an integral aspect of automation and calibration for a variety of experimental and computational applications. A simple algorithmic approach was implemented to analyze the movement of objects (grinding balls) between adjacent frames and evaluate the velocities of the balls.…”