Landmark detection with surprise saliency using convolutional neural networks

@article{Tang2016LandmarkDW,
  title={Landmark detection with surprise saliency using convolutional neural networks},
  author={Feng Tang and Damian M. Lyons and Daniel D. Leeds},
  journal={2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
  year={2016},
  pages={204-211}
}
Landmarks can be used as a reference to enable people or robots to localize themselves or to navigate in their environment. Automatic definition and extraction of appropriate landmarks from the environment has proven to be a challenging task when pre-defined landmarks are not present. We propose a novel computational model of automatic landmark detection from a single image without any pre-defined landmark database. The hypothesis is that if an object looks abnormal due to its atypical scene… CONTINUE READING

Similar Papers

References

Publications referenced by this paper.
SHOWING 1-10 OF 37 REFERENCES

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2015
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

ImageNet: A large-scale hierarchical image database

  • CVPR 2009
  • 2009
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Fast R-CNN

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

The image of the city

K. Lynch
  • MIT press,
  • 1960
VIEW 2 EXCERPTS
HIGHLY INFLUENTIAL

You Only Look Once: Unified, Real-Time Object Detection

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
VIEW 2 EXCERPTS