Machine Learning for High-Speed Corner Detection

@inproceedings{Rosten2006MachineLF,
  title={Machine Learning for High-Speed Corner Detection},
  author={Edward Rosten and Tom Drummond},
  booktitle={ECCV},
  year={2006}
}
Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7% of the available processing time. By… CONTINUE READING
Highly Influential
This paper has highly influenced 255 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 2,722 citations. REVIEW CITATIONS
1,629 Citations
34 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 1,629 extracted citations

2,723 Citations

0100200300'06'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 2,723 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 34 references

Demo software: Sift keypoint detector

  • D. G. Lowe
  • http://www.cs.ubc.ca/~lowe/keypoints/
  • 2005
2 Excerpts

Similar Papers

Loading similar papers…