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3D Polynomial Interpolation Based Local Binary Descriptor

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 50))

Abstract

Many efforts are devoted to develop binary descriptors due to their low complexity, and flexibility in case of embedded systems. Almost all works on binary descriptor conception didn’t exploit all information of a given patch; they just involved pixels intensities into binary test process. This kind of solution lack efficiency on patch description. In this paper, we propose to design a new descriptor based on 3D polynomial interpolation by used pixels intensities. We must take into account geometric positions of pixels. We suggest to divide the patch into equal grid cells (sub patches). Each sub patch undergoes a dimension augmentation. It becomes a 3-dimensional vector by considering intensities values as the third dimension. Based on 3D polynomial interpolation, we approximate the point cloud by a surface. This step is followed by a binary tests between all coefficients of polynomials situated in neighborhoods. Our method shows a considerable discrimination in case of high similarity. The results of our approach are evaluated on a well-known benchmark dataset exhibit a considerable robustness and reliability in front of severe changes. A computation costing is reported in the end of results section.

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References

  1. Lowe, G.: Distinctive image features from scale-invariant keypoint. Int. J. Comput. Vis 60(2), 91–110 (2004)

    Article  Google Scholar 

  2. Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal sets for efficient structure from motion. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 2. Publisher (2008)

    Google Scholar 

  3. Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Vis. Comput. Graph. 22(12), 2633–2651 (2016)

    Article  Google Scholar 

  4. Klein, G.G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of IEEE ACM International Symposium Mixed Augmented Reality. Japan, pp. 225–234 (2007)

    Google Scholar 

  5. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  6. Bellarbi, A., Otmane, S., Zenati, N., Benbelkacem, S.: MOBIL: a moments based local binary descriptor. In: International Symposium on Mixed and Augmented Reality (ISMAR 2014), pp. 251–252. IEEE, Munich (2014)

    Google Scholar 

  7. Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  8. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision, ECCV 2010, LNCS, vol. 6314, pp. 778–792. Springer, Germany (2010)

    Google Scholar 

  9. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2011), pp. 2548–2555. IEEE, Spain (2011)

    Google Scholar 

  10. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2011), pp. 2564–2571. IEEE, Spain (2011)

    Google Scholar 

  11. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory IT 8, 179–187 (1962)

    Google Scholar 

  12. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retinal keypoint. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 510–517. IEEE (2012)

    Google Scholar 

  13. Yang, X., Cheng, K.T.: LDB: an ultra-fast feature for scalable augmented reality on mobile devices. In: International Symposium on Mixed and Augmented Reality (ISMAR), pp. 49–57. IEEE (2012)

    Google Scholar 

  14. Bellarbi, A., Zenati, N., Otmane, S., Belghit, H.: Learning moment-based fast local binary descriptor. J. Electron. Imaging 26(2), 1017–9909 (2017)

    Article  Google Scholar 

  15. Farin, G.: Courbes et surfaces pour la CGAO. Masson, Paris (1992)

    Google Scholar 

  16. Besl, P.J.: Geometric modeling and computer vision. Proc. IEEE 76(8), 936–958 (1988)

    Google Scholar 

  17. Moron, V.: Mise en correspondance de données 3D avec un model CAO: application à l’inspection automatique. D. thesis, Dept. Auto Ind, Lyon Univ., France, (1996)

    Google Scholar 

  18. Faux, I.D., Pratt, M.: Computational Geometry for Design and Manufacture. Ellis Harwood Series in Mathematics and its applications. Halsted Press, Chichester (1981)

    Google Scholar 

  19. Rosten, E., Drummond, T.: Machine learning for high speed corner detection. In: European Conference on Computer Vision (ECCV), vol. 1 (2006)

    Google Scholar 

  20. Demailly, J.P.: Analyse numérique et équations différentielles. EDP sciencies, Grenoble (2006)

    Google Scholar 

  21. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Google Scholar 

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Correspondence to Elhaouari Kobzili .

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Kobzili, E., Larbes, C., Allam, A., Demim, F. (2019). 3D Polynomial Interpolation Based Local Binary Descriptor. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_22

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