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Efficient invariant interest point detector using Bilateral-Harris corner detector for object recognition application

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Abstract

Interest point detection plays a significant role in computer vision applications. The most commonly used interest point detector algorithm is scale invariant feature transform (SIFT). The use of Gaussian filter in the SIFT algorithm fails to match interest points on the edge and it also causes blur annoyance in the rescaling process. To overcome this failure Bilateral-Harris Corner Detector (BHCD) has been proposed in this paper. In the proposed BHCD, a Bilateral filter preserves edges by smoothening and removing noise in an image. Accuracy in localization of interest points are improved by using the proposed dynamic blur metric calculation. The Harris corner has been added to get stable and reliable interest point detection. The proposed BHCD has been simulated for the evaluation criteria such as repeatability and matching score. Extensive experimental results show that the proposed method is more robust to illumination, scaling, rotation, compression and viewpoint changes. The experimental evaluation for BHCD has been carried for the object recognition benchmark datasets COIL-100, ZuBud, Caltech-101. The proposed BHCD achieves highest recognition rate compared to the other state-of-the-art methods.

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References

  1. Agrawal M, Konolige K, Blas MR (2008) CenSurE: center surround Extremas for Realtime feature detection and matching. In: Forsyth D, Torr P, Zisserman A (eds) ECCV 2008, Part IV. LNCS, vol 5305. Springer, Heidelberg, pp 102–115

    Google Scholar 

  2. Bay H, Tuytelaars T, van Gool L (2008) Speeded up robust features. Comput Vis Image Underst 110(3):346–359. doi:10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  3. Brown M, Lowe DG (2003) Recognising Panoramas. Proceedings of the Ninth IEEE International Conference on Computer Vision 2:1218–1225. doi:10.1109/ICCV.2003.1238630

    Article  Google Scholar 

  4. Chang L, Duarte MM, Sucar LE, Morales EF (2010) Object class recognition using SIFT and Bayesian networks. Advances in Soft Computing, Volume 6438 of the series Lecture Notes in Computer Science, pp 56–66, Springer Berlin Heidelberg

  5. Crete F, Dolmiere T, Ladret P, Nicolas M (2007) The blur effect: perception and estimation with a new no-reference perceptual blur metric. SPIE Human Vision & Electronic Imaging XII, San Jose, United States, vol 6492, pp 64920I–11. doi:10.1117/12.702790

  6. Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611. doi:10.1109/TPAMI.2006.79

    Article  Google Scholar 

  7. Fernandez P, Bartoli A, Davison AJ (2012) KAZE Features. In: Proceedings of European Conference on Computer Vision (ECCV), Part VI, Lecture Notes in Computer Science, vol 7577, Florence, Italy, pp 214–227

  8. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proc. of Fourth Alvey Vision Conference, pp 189–192. doi:10.5244/c.2.23

  9. Huang M, Mu Z, Zeng H, Huang H (2015) A novel approach for interest point detection via laplacian-of-bilateral filter. Journal of Sensors, Hindawi Publishing Corporation 2015:9. Article ID 685154. doi:10.1155/2015/685154

  10. Krig S (2014) Interest point detector and feature descriptor survey. Computer Vision Metrics, Springer, pp 217–282

  11. Leutenegger S, Chli M, Siegwart RY 2011) BRISK: binary robust invariant scalable Keypoints. In: roceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, Barcelona, pp 2548–2555. doi:10.1109/ICCV.2011.6126542

  12. Lowe DG (1999) Object recognition from local scale-invariant features. International Conference on Computer Vision, Corfu, pp 1150–1157. doi:10.1109/ICCV.1999.790410

  13. Lowe DG (2004) Distinctive image features from scale-invariant interest points. Int J Comput Vis 60(2):91–110. doi:10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  14. Matas J, Chum O, Urban M, Pajdla T (2004) Robust wide-base-line stereo from maximally stable extremal regions. Image Vis Comput 22(10):761–767. doi:10.1016/j.imavis.2004.02.006

    Article  Google Scholar 

  15. Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. Int J Comput Vis 60(1):63–86. doi:10.1023/B:VISI.0000027790.02288.f2

    Article  Google Scholar 

  16. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630. doi:10.1109/TPAMI.2005.188

    Article  Google Scholar 

  17. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72. doi:10.1007/s11263-005-3848-x

    Article  Google Scholar 

  18. Nayar SK, Nene SA and Murase H (1996) Columbia object image library (COIL-100). Dept. Comput. Sci., Columbia Univ., New York, NY, USA, Tech. Rep. CUCS-006-96

  19. Schmid C, Mohr R, Bauckhage C (2000) Evaluation of interest point detectors. Int J Comput Vis 37(2):151–172. doi:10.1023/A:1008199403446

    Article  MATH  Google Scholar 

  20. Se S, Lowe David G, Little J (2001) Vision-based mobile robot localization and mapping using scale-invariant features. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (Cat. No.01CH37164), vol 2, pp 2051–2058. doi:10.1109/ROBOT.2001.932909

  21. Shao H, Svoboda T, Van Gool L (2003) ZuBuD — Zurich Buildings Database for Image Based Recognition. Technical Report No. 260, ETH Zurich, Computer Vision Laboratory, Swiss Federal Institute of Technology

  22. Sirmacek B, Unsalan C (2009) Urban area and building detection using SIFT Keypoints and graph theory. IEEE Trans Geosci Remote Sens 47(4):1156–1167. doi:10.1109/TGRS.2008.2008440

    Article  Google Scholar 

  23. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: The proceedings of 6th IEEE Int. Conf. Computer Vision (IEEE Cat. No.98CH36271), Bombay, pp 834–846. doi:10.1109/ICCV.1998.710815

  24. Zhou H, Yuan Y, Shi C (2009) Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding, Special Issue on Video Analysis 113(3):345–352. doi:10.1016/j.cviu.2008.08.006

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamil Nadu, 641 013, India

    R. Manoranjitham & P. Deepa

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  1. R. Manoranjitham
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  2. P. Deepa
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Correspondence to R. Manoranjitham.

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Manoranjitham, R., Deepa, P. Efficient invariant interest point detector using Bilateral-Harris corner detector for object recognition application. Multimed Tools Appl 77, 9365–9378 (2018). https://doi.org/10.1007/s11042-017-4982-5

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