Lastly, we propose a spatio-temporal feature aggregation module to enrich the latent features and a consistency loss to increase the robustness of the overall pipeline. We conduct comprehensive experiments to verify our method’s effectiveness where still objects are retained, but real occluded objects are discarded.
The MSMO algorithm uses the velocities of each event to create an average of the scene and filter out dissimilar events. This work shows the study performed on the velocity values of the events and explains why ultimately an average-based velocity filter is insufficient for lightweight MSMO detection and tracking of objects using an EBS camera.
The work explores bio-inspired applications for tasks where frame-based methods are already successful but present robustness flaws because classical frame-based imagers cannot be intrinsically high speed and high dynamic range.
This paper presents a novel fusion of low-level approaches for dimensionality reduction into an effective approach for high-level objects in neuromorphic camera data called Inceptive Event Time-Surfaces (IETS).
We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. We open up new possibilities for online and on-chip learning in flow cytometry applications.