TR2017-066

ROS2D: Image Feature Detector Using Rank Order Statistics


    •  Yousif, K., Taguchi, Y., Ramalingam, S., Bab-Hadiashar, A., "ROS2D: Image Feature Detector Using Rank Order Statistics", IEEE International Conference on Robotics and Automation (ICRA), DOI: 10.1109/​ICRA.2017.7989524, May 2017.
      BibTeX TR2017-066 PDF
      • @inproceedings{Yousif2017may,
      • author = {Yousif, Khalid and Taguchi, Yuichi and Ramalingam, Srikumar and Bab-Hadiashar, Alireza},
      • title = {ROS2D: Image Feature Detector Using Rank Order Statistics},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2017,
      • month = may,
      • doi = {10.1109/ICRA.2017.7989524},
      • url = {https://www.merl.com/publications/TR2017-066}
      • }
  • Research Areas:

    Computer Vision, Robotics

Abstract:

We present a new image feature detection method. Our method selects features based on segmenting points with high local intensity variations across different scales using a robust rank order statistics approach. Our method produces a large number of repeatable features that are invariant to several image transformations such as rotation, scaling, viewpoint, and lighting variations. We show the advantages of our feature in comparison to other existing features using the Oxford dataset. We also show that, when used in monocular and stereo SLAM systems, our feature outperforms SIFT in terms of the pose estimation accuracy using several public datasets including the KITTI dataset.