Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features

Grzegorz Kurzejamski, Jacek Zawistowski, Grzegorz Sarwas

2015

Abstract

This paper presents method for analysis of the vote space created from the local features extraction process in a multi-detection system. The method is opposed to the classic clustering approach and gives a high level of control over the clusters composition for further verification steps. Proposed method comprises of the graphical vote space presentation, the proposition generation, the two-pass iterative vote aggregation and the cascade filters for verification of the propositions. Cascade filters contain all of the minor algorithms needed for effective object detection verification. The new approach does not have the drawbacks of the classic clustering approaches and gives a substantial control over process of detection. Method exhibits an exceptionally high detection rate with conjunction with a low false detection chance in comparison with alternative methods.

References

  1. (2014). Classifier tool for opencv and fann v. 4.11.8. http://classifieropencv.codeplex.com/. Accessed: 2014-12-18.
  2. Alahi, A., Ortiz, R., and Vandergheynst, P. (2012). FREAK: Fast retina keypoint. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 510-517.
  3. Azad, P., Asfour, T., and Dillmann, R. (2009). Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pages 4275-4280.
  4. Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3):346 - 359. Similarity Matching in Computer Vision and Multimedia.
  5. Blaschko, M. and Lampert, C. (2008). Learning to localize objects with structured output regression. In Forsyth, D., Torr, P., and Zisserman, A., editors, Computer Vision ECCV 2008, volume 5302 of Lecture Notes in Computer Science, pages 2-15. Springer Berlin Heidelberg.
  6. Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). BRIEF: Binary robust independent elementary features. In Daniilidis, K., Maragos, P., and Paragios, N., editors, Computer Vision ECCV 2010, volume 6314 of Lecture Notes in Computer Science, pages 778-792. Springer Berlin Heidelberg.
  7. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893 vol. 1.
  8. Felzenszwalb, P., Girshick, R., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1627-1645.
  9. Iwanowski, M., ZieliÁski, B., Sarwas, G., and Stygar, S. (2014). Identification of products on shop-racks by morphological preprocessing and feature-based detection. In Chmielewski, L., Kozera, R., Shin, B.-S., and Wojciechowski, K., editors, Computer Vision and Graphics, volume 8671 of Lecture Notes in Computer Science, pages 286-293. Springer International Publishing.
  10. Ke, Y. and Sukthankar, R. (2004). PCA-SIFT: a more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II-506-II513 Vol.2.
  11. Kurzejamski, G., Zawistowski, J., and Sarwas, G. (2014). Apparatus and method for multi-object detection in a digital image. EU Patent 14461566.3.
  12. Lampert, C. H., Blaschko, M., and Hofmann, T. (2008). Beyond sliding windows: Object localization by efficient subwindow search. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8.
  13. Leutenegger, S., Chli, M., and Siegwart, R. (2011). BRISK: Binary robust invariant scalable keypoints. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2548-2555.
  14. Lowe, D. (1999). Object recognition from local scaleinvariant features. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1150-1157 vol.2.
  15. Lowe, D. (2004). Distinctive image features from scaleinvariant keypoints. volume 60, pages 91-110. Kluwer Academic Publishers.
  16. Morel, J.-M. and Yu, G. (2009). Asift: A new framework for fully affine invariant image comparison. SIAM J. Img. Sci., 2(2):438-469.
  17. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2564-2571.
  18. Sarwas, G. and Skoneczny, S. (2015). Object localization and detection using variance filter. In Choras, R. S., editor, Image Processing & Communications Challenges 6, volume 313 of Advances in Intelligent Systems and Computing, pages 195-202. Springer International Publishing.
  19. Shi, J. and Tomasi, C. (1994). Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR 7894., 1994 IEEE Computer Society Conference on, pages 593-600.
  20. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages 511-518.
  21. Yeh, T., Lee, J., and Darrell, T. (2009). Fast concurrent object localization and recognition. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 280-287.
  22. Zickler, S. and Efros, A. (2007). Detection of multiple deformable objects using PCA-SIFT. In Proceedings of the 22Nd National Conference on Artificial Intelligence - Volume 2, AAAI'07, pages 1127-1132. AAAI Press.
  23. Zickler, S. and Veloso, M. M. (2006). Detection and localization of multiple objects. In Humanoid Robots, 2006 6th IEEE-RAS International Conference on, pages 20-25.
Download


Paper Citation


in Harvard Style

Kurzejamski G., Zawistowski J. and Sarwas G. (2015). Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 252-259. DOI: 10.5220/0005267002520259


in Bibtex Style

@conference{visapp15,
author={Grzegorz Kurzejamski and Jacek Zawistowski and Grzegorz Sarwas},
title={Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={252-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005267002520259},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features
SN - 978-989-758-090-1
AU - Kurzejamski G.
AU - Zawistowski J.
AU - Sarwas G.
PY - 2015
SP - 252
EP - 259
DO - 10.5220/0005267002520259