METHOD OF EXTRACTING INTEREST POINTS BASED ON MULTI-SCALE DETECTOR AND LOCAL E-HOG DESCRIPTOR

Manuel Grand-brochier, Christophe Tilmant, Michel Dhome

Abstract

This article proposes an approach to extraction (detection and description) of interest points based Fast-Hessian and E-HOG. SIFT and SURF are the two most used methods for this problem and their studies allow us to understand their construction and extract the various advantages (invariances, speeds, repeatability). Our goal is, firstly, to couple these advantages to create a new system (detector, descriptor and matching) and, secondly, to determine the characteristic points for different applications (image transformation, 3D reconstruction...). Our system must also be as invariant as possible for the image transformation (rotations, scales, viewpoints for example). Finally, we have to find a compromise between a good matching rate and the number of points matched. All the detector and descriptor parameters (orientations, thresholds, analysis shape) will be also detailed in this article.

References

  1. Arya, S., Mount, D., Netanyahu, N., Silverman, R., and Wu, A. (1998). An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. J.ACM, 45:891-923.
  2. Bauer, J., Snderhauf, N., and Protzel, P. (2007). Comparing several implementations of two recently published feature detectors. Intelligent Autonomous Vehicles.
  3. Bay, H., Tuylelaars, T., and Gool, L. V. (2006). Surf : Speeded up robust features. European Conference on Computer Vision.
  4. Choksuriwong, A., Laurent, H., and Emile, B. (2005). Etude comparative de descripteur invariants d'objets. ORASIS.
  5. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. IEEE Conference on Computer Vision and Pattern Recognition.
  6. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. Alvey Vision Conference, pages 147- 151.
  7. Juan and Gwun (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing, 3(4):143-152.
  8. Li, J. and Allison, N.M. (2008). A Comprehensive Review of Current Local Features for Computer Vision. Neurocomputing, 71(10-12):1771-1787.
  9. Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2):79-116.
  10. Lowe, D. (1999). Object recognition from local scaleinvariant features. IEEE International Conference on Computer Vision, pages 1150-1157.
  11. Lowe, D. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  12. Mikolajczyk, K., and Schmid, C. (2002). An affine invariant interest point detector. European Conference on Computer Vision, 1:128-142.
  13. Mikolajczyk, K. and Schmid, C. (2004a). A performance evaluation of local descriptors. IEEE Pattern Analysis and Machine Intelligence, 27(10):1615-1630.
  14. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Van Gool, L. (2005). A comparaison of affine region detectors. International Journal of Computer Vision, 65(1/2):43- 72.
  15. Mikolajczyk, K. and Schmid, C. (2004b). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 1(60):63-86.
  16. Tola, E., Lepetit, V., and Fua, P. (2008). A fast local descriptor for dense matching. IEEE Conference on Computer Vision and Pattern Recognition.
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Paper Citation


in Harvard Style

Grand-brochier M., Tilmant C. and Dhome M. (2011). METHOD OF EXTRACTING INTEREST POINTS BASED ON MULTI-SCALE DETECTOR AND LOCAL E-HOG DESCRIPTOR . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 59-66. DOI: 10.5220/0003369300590066


in Bibtex Style

@conference{visapp11,
author={Manuel Grand-brochier and Christophe Tilmant and Michel Dhome},
title={METHOD OF EXTRACTING INTEREST POINTS BASED ON MULTI-SCALE DETECTOR AND LOCAL E-HOG DESCRIPTOR},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={59-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003369300590066},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - METHOD OF EXTRACTING INTEREST POINTS BASED ON MULTI-SCALE DETECTOR AND LOCAL E-HOG DESCRIPTOR
SN - 978-989-8425-47-8
AU - Grand-brochier M.
AU - Tilmant C.
AU - Dhome M.
PY - 2011
SP - 59
EP - 66
DO - 10.5220/0003369300590066