A Fast, Robust and Low Bit-Rate Representation for SIFT and SURF Features (bibtex)
by Martin Stommel, Matthias Langer, Otthein Herzog, Klaus-Dieter Kuhnert
Abstract:
SIFT features have become extremely popular in computer vision because of their reliable matching qualities under changing lighting conditions. In Robotics they are ubiquitious in self localisation and mapping (SLAM), object tracking and recognition. However, the length of the descriptor is a major obstacle for realtime applications and mobile platforms where the computation time and storage capacity is limited. It has also been shown that the high-dimensional SIFT descriptors suffer from a numerical instability known as the curse of dimensionality. Therefore, we investigate low bit-rate representations based on a binarisation of SIFT and SURF features. Our method is able to reduce the descriptor length by a factor of 8?32 and lifts the curse of dimensionality. The new descriptor is tested in a series of robotics related experiments, namely stereo analysis and object tracking under outdoor conditions, as well as image recognition. Our experiments show that the new representation is not only extremely compact, but also yields faster, more accurate and more robust results.
Reference:
Martin Stommel, Matthias Langer, Otthein Herzog, Klaus-Dieter Kuhnert, "A Fast, Robust and Low Bit-Rate Representation for SIFT and SURF Features", In SSRR, Kyoto, Japan, 2011.
Bibtex Entry:
@INPROCEEDINGS{Stommelc,
  author = {Stommel, Martin and Langer, Matthias and Herzog, Otthein and Kuhnert,
	Klaus-Dieter},
  title = {A Fast, Robust and Low Bit-Rate Representation for SIFT and SURF
	Features},
  booktitle = SSRR,
  year = {2011},
  address = {Kyoto, Japan},
  month = {November1--5},
  abstract = {SIFT features have become extremely popular in computer vision because
	of their reliable matching qualities under changing lighting conditions.
	In Robotics they are ubiquitious in self localisation and mapping
	(SLAM), object tracking and recognition. However, the length of the
	descriptor is a major obstacle for realtime applications and mobile
	platforms where the computation time and storage capacity is limited.
	It has also been shown that the high-dimensional SIFT descriptors
	suffer from a numerical instability known as the curse of dimensionality.
	Therefore, we investigate low bit-rate representations based on a
	binarisation of SIFT and SURF features. Our method is able to reduce
	the descriptor length by a factor of 8?32 and lifts the curse of
	dimensionality. The new descriptor is tested in a series of robotics
	related experiments, namely stereo analysis and object tracking under
	outdoor conditions, as well as image recognition. Our experiments
	show that the new representation is not only extremely compact, but
	also yields faster, more accurate and more robust results.},
  owner = {pmania},
  timestamp = {2012.11.06}
}
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