NOVEL PERSPECTIVES ON PATTERN MATCHING WITH ASSOCIATION-BASED SYMMETRIC LOCAL FEATURES

Deep S. Dev,∗ Dakshina R. Kisku,∗∗ and Phalguni Gupta∗∗∗

References

  1. [1] J.P. Lewis, Fast template matching, Proc. Vision Interface,Canadian Image Processing and Pattern Recognition Society,Quebec City, Canada, 1995, 120–123.
  2. [2] T. Mahalakshmi, R. Muthaiah, and P. Swaminathan, Anoverview of template matching method in image processing,Research Journal of Applied Sciences, Engineering and Tech-nology, 4(24), 2012, 5469–5473.
  3. [3] W. Zhao and R. Chellappa, Face recognition: A literaturesurvey, ACM Computing Surveys, 35(4), 2003, 399–458.
  4. [4] W. Ouyang, R. Zhang, and W.K. Cham, Fast pattern matchingusing orthogonal Haar transform, Proc. IEEE Conf. ComputerVision and Pattern Recognition (CVPR), San Francisco, CA,2010, 3050–3057.
  5. [5] S. Haykin, Neural networks – A comprehensive foundation,2nd ed. (Upper Saddle River, NJ, United States: Prentice-Hall,1998), 842.
  6. [6] Y. Li, H. Li, and Z. Cai, Fast orthogonal Haar transformpattern matching via image square sum, IEEE Transactionson Pattern Analysis and Machine Intelligence, 36(9), 2014,1748–1760.
  7. [7] X. Tan and B. Triggs, Enhanced local texture feature setsfor face recognition under difficult lighting conditions, IEEETransaction Image Processing, 19(6), 2010, 1635–1650.
  8. [8] R. Kumar, Orientation local binary pattern based fingerprintmatching, International Journal of SN Computer Science, 1(2),2020, article no. 67, doi: 10.1007/s42979-020-0068-y.
  9. [9] P. Banerjee, A.K. Bhunia, A. Bhattacharyya, P.P. Roy, and S.Murala, Local Neighborhood Intensity Pattern: A new texturefeature descriptor for image retrieval, Expert Systems withApplications, 113, 2018, 100–115.
  10. [10] W. Ouyang and W.K. Cham, Fast algorithm for WalshHadamard transform on sliding windows, IEEE Transactionson Pattern Analysis and Machine Intelligence, 32(1), 2010,165–171.
  11. [11] C. Lampert, M. Blaschko, and T. Hofmann, Beyond slidingwindows: Object localization by efficient subwindow, Proc.IEEE Conference on Computer Vision and Pattern Recogni-tion, Anchorage, AK, 2008.
  12. [12] S. Korman, D. Reichman, G. Tsur, and S. Avidan, FAsT match:Fast affine template matching, IEEE Conf. on Computer Visionand Pattern Recognition, Portland, 2013.
  13. [13] K. Ahuja and P. Tuli, Object recognition by template matchingusing correlations and phase angle method, International Jour-nal of Advanced Research in Computer and CommunicationEngineering, 2(3), 2013, 1768–1773.
  14. [14] B. Froba and A. Ernst, Face detection with the modified censustransform, Proc. of the 6th IEEE International Conference onAutomatic Face and Gesture Recognition, Seoul, 2004, 91–96.
  15. [15] L. Zhang, R. Chu, S. Xiang, S. Liao, and S. Z. Li, Facedetection based on multi-block LBP representation, Proc. of theIAPR/IEEE International Conference on Biometrics, Seoul,South Korea, 2007, 11–18.
  16. [16] H. Zhang, W. Gao, X. Chen, and D. Zhao, Object detectionusing spatial histogram features, Image and Vision Computing,24(4), 2006, 327–341.
  17. [17] S. Yan, S. Shan, X. Chen, and W. Gao, Locally assembledbinary (LAB) feature with feature-centric cascade for fastand accurate face detection, Proc. of IEEE Computer SocietyConference on Computer Vision and Pattern Recognition,Anchorage, AK, 2008, 1–7.
  18. [18] Z. Guo, L. Zhang, and D. Zhang, Rotation invariant textureclassification using LBP variance with global matching, Journalof Pattern Recognition, 43(3), 2010, 706–719.
  19. [19] Y. Kobayashi, T. Okamoto, and M. Onishi, Generation ofobstacle avoidance based on image features and embodiment,International Journal of Robotics and Automation, 24(4), 2012,364–376.
  20. [20] D. Ren, J. Chen, C. Zhang, Z. Liu, X. Liu, and H. Zhou, Anadaptive illumination pre-processing method for face recogni-tion, International Journal of Robotics and Automation, 32(5),2017, 509–516.
  21. [21] J. Chen and B. Tiddeman, Multi-cue facial feature detectionand tracking under various illuminations, International Journalof Robotics and Automation, 25(2), 2010, 162–171.
  22. [22] Z. Dan, X. Wei, S. Sun, and G. Zhou, An improved destripingmethod for remote sensing images, International Journal ofRobotics and Automation, 33(1), 2018, 104–110.
  23. [23] D. Yang, A. Lu, and J. Wang, Classification of cookedbeef, lamb, and pork using hyperspectral imaging, Interna-tional Journal of Robotics and Automation, 33(3), 2018, doi:10.2316/Journal.206.2018.3.206-5440.11
  24. [24] M. Yan, B. Huang, D. Zhu, and S.X. Yang, A novel segmen-tation method based on grayscale wave for underwater images,International Journal of Robotics and Automation, 33(4), 2018,doi: 10.2316/Journal.206.2018.4.206-5095.
  25. [25] J. Han, H. Wang, M. Gao, and M. Sun, A super pixel-basedautomatic classification method for polarimetric SAR image,International Journal of Robotics and Automation, 34(6), 2019,doi: 10.2316/J.2019.206-0073.
  26. [26] J. Yang, G. Sha, Y. Zhou, G. Wang, and B. Zheng, Statisticalpattern recognition for structural health monitoring using ESNfeature extraction method, International Journal of Roboticsand Automation, 33(6), 2018, 569–576.
  27. [27] S.A. Nene, S.K. Nayar, and H. Murase, Columbia object imagelibrary (COIL-100), Technical Report CUCS-006-96, ColumbiaUniversity, New York, 1996.
  28. [28] G. Wang, Y. Zhang, and L. Fei-Fei, Using dependant regions orobject categorization in a generative framework, IEEE Conf.on Computer Vision and Pattern Recognition, New York, NY,2006.

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