  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). IETSs overcome several
limitations of conventional time-surfaces by increasing robustness to noise,
promoting spatial consistency, and improving the temporal localization of
(moving) edges. Combining IETS with transfer learning improves state-of-the-art
performance on the challenging problem of object classification utilizing event
camera data.
