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

@article{AndradeLoarca2020ShearletsAF,
  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},
  year={2020},
  volume={476}
}
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
6 Citations

References

SHOWING 1-10 OF 59 REFERENCES
CASENet: Deep Category-Aware Semantic Edge Detection
Semantic Edge Detection with Diverse Deep Supervision
Richer Convolutional Features for Edge Detection
Richer Convolutional Features for Edge Detection
Simultaneous Edge Alignment and Learning
DeepEdge: A multi-scale bifurcated deep network for top-down contour detection
On Edge Detection
  • V. Torre, T. Poggio
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1986
Semantic contours from inverse detectors
...
1
2
3
4
5
...