Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks

@article{Maninis2018ConvolutionalOB,
  title={Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks},
  author={Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Pablo Arbelx00E1ez and Luc van Gool},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2018},
  volume={40},
  pages={819-833}
}
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to… CONTINUE READING
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