Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

  title={Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm},
  author={H{\'e}ctor Andrade-Loarca and G. Kutyniok and O. {\"O}ktem},
  journal={Proceedings. Mathematical, Physical, and Engineering Sciences},
Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox… Expand
7 Citations
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Shearlets: From Theory to Deep Learning
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Semantic Edge Detection with Diverse Deep Supervision
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Richer Convolutional Features for Edge Detection
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Richer Convolutional Features for Edge Detection
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On Edge Detection
  • V. Torre, T. Poggio
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1986
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