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A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition
[article]
2017
arXiv
pre-print
This paper presents a novel hierarchical spatiotemporal orientation representation for spacetime image analysis. It is designed to combine the benefits of the multilayer architecture of ConvNets and a more controlled approach to spacetime analysis. A distinguishing aspect of the approach is that unlike most contemporary convolutional networks no learning is involved; rather, all design decisions are specified analytically with theoretical motivations. This approach makes it possible to
arXiv:1708.06690v1
fatcat:qrfwj3guurbxphjv5b5j2qgxra