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Filter Learning for Linear Structure Segmentation

Authors
  • Rigamonti, Roberto
  • Türetken, Engin
  • González Serrano, Germán
  • Fua, Pascal
  • Lepetit, Vincent
Publication Date
Jan 01, 2011
Source
Infoscience @ EPFL
Keywords
License
Unknown
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Abstract

We introduce an approach to learning convolution filters whose joint output can be fed to a classifier that labels them as belonging to linear structures or not. The filters are learned using sparse synthesis techniques but we show that enforcing constraints is not required at run-time to achieve good classification performance. In practice, this is important as it drastically reduces the computational cost. We show that our approach outperforms the state-of-the-art on difficult, and very different, images of roads, retinal scans, and dendritic networks.

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