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High-dimensional convolutional networks for geometric pattern recognition
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Cited 25 time in Scopus
- Authors
- Issue Date
- 2020
- Publisher
- IEEE
- Citation
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.11224-11233
- 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.
- ISSN
- 1063-6919
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