English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we focus on using convolution neural networks (CNN) to performobject recognition on the event data. In object recognition, it is importantfor a neural network to be robust to the variations of the data during testing.For traditional cameras, translations are well handled because CNNs arenaturally equivariant to translations. However, because event cameras recordthe change of light intensity of an image, the geometric shape of event volumeswill not only depend on the objects but also on their relative motions withrespect to the camera. The deformation of the events caused by motions causesthe CNN to be less robust to unseen motions during inference. To address thisproblem, we would like to explore the equivariance property of CNNs, awell-studied area that demonstrates to produce predictable deformation offeatures under certain transformations of the input image.

Motion Equivariance OF Event-based Camera Data with the Temporal Normalization Transform