March 29, 2024, 4:43 a.m. | Dmitrii Zhemchuzhnikov, Sergei Grudinin

cs.LG updates on

arXiv:2403.19612v1 Announce Type: cross
Abstract: Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis. Volumetric data applications seek techniques ensuring invariance not only to shifts but also to pattern rotations. While traditional methods can readily achieve translational invariance, rotational invariance possesses multiple challenges and remains an active area of research. Here, we present ILPO-Net (Invariant to Local Patterns Orientation Network), a novel approach that handles arbitrarily shaped patterns with the convolutional operation inherently …

arxiv cs.lg network patterns recognition type

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