On the Use of Feature Descriptors on Raw Image Data

Alina Trifan, António J. R. Neves

Abstract

Local feature descriptors and detectors have been widely used in computer vision in the last years for solving object detection and recognition tasks. Research efforts have been focused on reducing the complexity of these descriptors and improving their accuracy. However, these descriptors have not been tested until now on raw image data. This paper presents a study on the use of two of the most known and used feature descriptors, SURF and SIFT, directly on raw CFA images acquired by a digital camera. We are interested in understanding if the number and quality of the keypoints obtained from a raw image are comparable to the ones obtained in the grayscale images, which are normally used by these transforms. The results that we present show that the number and positions of the keypoints obtained from grayscale images are similar to the ones obtained from CFA images and furthermore to the ones obtained from grayscale images that resulted directly from the interpolation of a CFA image.

References

  1. Kodak image set. http://r0k.us/graphics/kodak/. Accessed: 2015-10-5.
  2. Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). BRIEF: Binary Robust Independent Elementary Features. 11th European Conference on Computer Vision (ECCV), Heraklion, Crete. LNCS Springer.
  3. H. Bay, A. E., Tuytelaars, T., and Gool, L. V. (2008). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3):346-359.
  4. Kimmel, R. (1999). Demosaicing: image reconstruction from color ccd samples. IMAGE PROCESSING, IEEE TRANSACTIONS ON.
  5. Larabi, S. and Setitra, I. (2015). A study on discrimination of sift feature applied to binary shapes. In Proc. of ECCOMAS Thematic Conferences on Computational Vision and Medical Image Processing VIP 2015, Santa Cruz de Tenerife, Spain, pages 295-301. Taylor and Francis.
  6. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  7. Miksik, O. and Mikolajczyk, K. (2012). Evaluation of local detectors and descriptors for fast feature matching. In ICPR, pages 2681-2684. IEEE.
  8. Muja, M. and Lowe, D. G. (2009). Fast approximate nearest neighbors with automatic algorithm configuration.
  9. International Conference on Computer Vision Theory and Applications (VISAPP), pages 331-340.
  10. Neves, A. and Trifan, A. (2015). Time-constrained detection of colored objects on raw bayer data. In Proc. of ECCOMAS Thematic Conferences on Computational Vision and Medical Image Processing VIP 2015, Santa Cruz de Tenerife, Spain, pages 288-294. Taylor and Francis.
  11. Rosten, E. and Drummond, T. (2006). Machine learning for high speed corner detection. 9th European Conference on Computer Vision (ECCV), 1:430-443.
Download


Paper Citation


in Harvard Style

Trifan A. and Neves A. (2016). On the Use of Feature Descriptors on Raw Image Data . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 655-662. DOI: 10.5220/0005756506550662


in Bibtex Style

@conference{icpram16,
author={Alina Trifan and António J. R. Neves},
title={On the Use of Feature Descriptors on Raw Image Data},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={655-662},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005756506550662},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - On the Use of Feature Descriptors on Raw Image Data
SN - 978-989-758-173-1
AU - Trifan A.
AU - Neves A.
PY - 2016
SP - 655
EP - 662
DO - 10.5220/0005756506550662