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- W4287777658 abstract "Many problems in science and engineering can be formulated in terms of geometric patterns in high-dimensional spaces. We present high-dimensional convolutional networks (ConvNets) for pattern recognition problems that arise in the context of geometric registration. We first study the effectiveness of convolutional networks in detecting linear subspaces in high-dimensional spaces with up to 32 dimensions: much higher dimensionality than prior applications of ConvNets. We then apply high-dimensional ConvNets to 3D registration under rigid motions and image correspondence estimation. Experiments indicate that our high-dimensional ConvNets outperform prior approaches that relied on deep networks based on global pooling operators." @default.
- W4287777658 created "2022-07-26" @default.
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- W4287777658 date "2020-05-16" @default.
- W4287777658 modified "2023-10-16" @default.
- W4287777658 title "High-dimensional Convolutional Networks for Geometric Pattern Recognition" @default.
- W4287777658 doi "https://doi.org/10.48550/arxiv.2005.08144" @default.
- W4287777658 hasPublicationYear "2020" @default.
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