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An efficient technique for object recognition using fractional Harris–Stephens corner detection algorithm

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Abstract

In this research, a fractional-order technique for corner detection and image matching based on the Harris-Stephens algorithm and the Caputo-Fabrizio and Atangana-Baleanu derivatives is proposed and experimentally tested. It focuses on three main ideas: 1) To suppress image noise more effectively while maintaining better image fidelity, a fractional Gaussian filter based on the Atangana-Baleanu derivative is designed. 2) The image derivatives and consequently the Hessian matrix are generalized through the Caputo-Fabrizio derivative, which has a high capability to preserve texture details in low-contrast regions. 3) An image-matching scheme that combines our fractional corner detector with the SURF algorithm is developed so that the corner extraction and the accuracy of matching images are improved. The proposed technique is compared experimentally with the conventional Harris-Stephens algorithm and some other methods reported in the literature. Experimental results on test images validated this approach in terms of more corners detected and matching accuracy improvement. In addition, the proposed operator is implemented for image processing of concrete structures images, i.e., for the identification and analysis of cracks in this kind of structure. Implementation results on images with different types of cracks prove the advantages of our operator over other methods since it can detect more pixels corresponding to cracks, improving their identification and the way they propagate, that is, their patterns of propagation.

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References

  1. Adams M (2019) The fractional harris-laplace feature detector, in: International Conference on Scale Space and Variational Methods in Computer Vision, Springer, pp 3–12

  2. Alkahtani BST (2016) Chua’s circuit model with atangana-baleanu derivative with fractional order. Chaos, Solitons Fractals 89:547–551

    Article  ADS  Google Scholar 

  3. An M, Kang D-S (2022) The distance measurement based on corner detection for rebar spacing in engineering images. J Supercomput 78(10):12380–12393

    Article  Google Scholar 

  4. Arora S, Mathur T, Agarwal S, Tiwari K, Gupta P (2022) Applications of fractional calculus in computer vision: A survey. Neurocomputing 489:407–428

    Article  Google Scholar 

  5. Artin E (2015) The gamma function, Courier Dover Publications

  6. Atangana A, Owolabi KM (2018) New numerical approach for fractional differential equations. Mathemat Modell Natural Phenomena 13(1):3

    Article  MathSciNet  Google Scholar 

  7. Baleanu D, Diethelm K, Scalas E, Trujillo JJ (2012) Fractional calculus: models and numerical methods, vol. 3, World Scientific

  8. Bao J, Jing J, Zhang W, Liu C, Gao T (2022) A corner detection method based on adaptive multidirectional anisotropic diffusion. Multimed Tools Appl 1–26

  9. Basha CZ, Reddy MRK, Nikhil KHS, Venkatesh P, Asish A (2020) Enhanced computer aided bone fracture detection employing x-ray images by harris corner technique, in: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), IEEE, pp 991–995

  10. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vision Image Underst 110(3):346–359

    Article  Google Scholar 

  11. Burger W, Burge MJ (2022) Corner detection, in: Digital Image Processing, Springer, pp 145–164

  12. Caputo M, Fabrizio M (2015) A new definition of fractional derivative without singular kernel. Progress Fractional Differ Appl 1(2):73–85

    Google Scholar 

  13. Chen He X, Yung NH (2008) Corner detector based on global and local curvature properties. Optical Eng 47(5):057008–057008

    Article  ADS  Google Scholar 

  14. Chen J, Zou L-h, Zhang J, Dou L-h (2009) The comparison and application of corner detection algorithms. J Multimed 4(6)

  15. Cvišić I, Marković I, Petrović I (2021) Recalibrating the kitti dataset camera setup for improved odometry accuracy, in: 2021 European Conference on Mobile Robots (ECMR), IEEE, pp 1–6

  16. Dalir M, Bashour M (2010) Applications of fractional calculus. Appl Math Sci 4(21):1021–1032

    MathSciNet  Google Scholar 

  17. Debnath L (2003) Recent applications of fractional calculus to science and engineering. Int J Math Math Sci 2003(54):3413–3442

    Article  MathSciNet  Google Scholar 

  18. Dong Y (2022) Faint moving small target detection based on optical flow method, in: 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), IEEE, pp 391–395

  19. Dorrego GA, Cerutti RA (2012) The k-mittag-leffler function. Int J Contemp Math Sci 7(15):705–716

    MathSciNet  Google Scholar 

  20. George J, Raj SG (2021) Leaf identification using harris corner detection, surf feature and flann matcher, Int. J. Innov. Technol. Explor Eng 8(8)

  21. Harris C, Stephens M et al. (1988) A combined corner and edge detector, in: Alvey vision conference, Citeseer

  22. He X-C, Yung NH (2004) Curvature scale space corner detector with adaptive threshold and dynamic region of support, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., vol. 2, IEEE, pp 791–794

  23. Kang J, Yoon H, Lee S, Lee S (2021) Sparse checkerboard corner detection from global perspective, in: 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, pp 12–17

  24. Kochubei A, Luchko Y (2019) Basic Theory, Walter de Gruyter GmbH & Co KG

  25. Lee BY, Kim YY, Yi S-T, Kim JK (2013) Automated image processing technique for detecting and analysing concrete surface cracks. Struct Infrastruct Eng 9(6):567–577

    Article  Google Scholar 

  26. Li C, Qian D, Chen Y (2011) On riemann-liouville and caputo derivatives. Discrete Dynamics Nature Soc 2011

  27. Loverro A et al (2004) Fractional calculus: history, definitions and applications for the engineer. Rapport Tech, Univeristy Notre Dame, Depart Aerospace Mech Eng, pp 1–28

    Google Scholar 

  28. Luo T, Shi Z, Wang P (2020) Robust and efficient corner detector using non-corners exclusion. Appl Sci 10(2):443

    Article  Google Scholar 

  29. Luo R, Guo H (2021) Application of the several common algorithms for corner detection to sonar image registration, in: Advances in Wireless Communications and Applications, Springer, pp 93–99

  30. Mohamed SA, Yasin JN, Haghbayan M-h, Miele A, Heikkonen J, Tenhunen H, Plosila J (2021) Dynamic resource-aware corner detection for bio-inspired vision sensors, in: 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, pp 10465–10472

  31. Moravec HP (1980) Obstacle avoidance and navigation in the real world by a seeing robot rover, Stanford University

  32. Oliveira DS, Capelas de Oliveira E (2019) On a caputo-type fractional derivative. Adv Pure Appl Math 10(2):81–91

    Article  MathSciNet  Google Scholar 

  33. Ostalczyk P (2015) Discrete fractional calculus: applications in control and image processing, vol. 4, World scientific

  34. Pan X, Zhu J, Yu H, Chen L, Liu Y, Li L (2021) Robust corner detection with fractional calculus for magnetic resonance imaging. Biomed Signal Process Control 63:102112

    Article  Google Scholar 

  35. Patel TP, Panchal SR, Student P (2014) Corner detection techniques: an introductory survey. Int J Eng Develop Res 2(4):3680–3686

    Google Scholar 

  36. Podlubny I (1998) Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications. Elsevier

    Google Scholar 

  37. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. European conference on computer vision, Springer 2006:430–443

    Google Scholar 

  38. Rosten E, Porter R, Drummond T (2008) Faster and better: A machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119

    Article  Google Scholar 

  39. Sarwas G, Skoneczny S, Kurzejamski G (2017) Fractional order method of image keypoints detection, in: 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), IEEE, pp 349–353

  40. Seada NA, Mostafa MG (2019) Automatic ostia detection in cta volume data: a comparative study. Int J Med Eng Inf 11(1):86–101

    Google Scholar 

  41. Sikka R (2022) Harris corner detection for eye extraction, in: Integrated Emerging Methods of Artificial Intelligence & Cloud Computing, Springer, pp 164–169

  42. Sikka P, Asati AR, Shekhar C (2021) Real time fpga implementation of a high speed and area optimized harris corner detection algorithm. Microprocess Microsyst 80:103514

    Article  Google Scholar 

  43. Solís-Pérez J, Gómez-Aguilar JF, Escobar-Jiménez RF, Reyes-Reyes J (2019) Blood vessel detection based on fractional hessian matrix with non-singular mittag-leffler gaussian kernel. Biomed Signal Process Control 54:101584

    Article  Google Scholar 

  44. Suárez PL, Sappa AD, Vintimilla BX (2018) Adaptive harris corner detector evaluated with cross–spectral images, in: International Conference on Information Technology & Systems, Springer, pp 732–744

  45. Sun X, Zhong B, Yang J, Ma K-K (2023) Corner detection via scale-space behavior-guided trajectory tracing. IEEE Signal Process Lett 30:50–54

    Article  ADS  Google Scholar 

  46. Tenreiro Machado J, Silva MF, Barbosa RS, Jesus IS, Reis CM, Marcos MG, Galhano AF (2010) Some applications of fractional calculus in engineering, Mathematical problems in engineering 2010

  47. Tissainayagam P, Suter D (2004) Assessing the performance of corner detectors for point feature tracking applications. Image Vision Comput 22(8):663–679

    Article  Google Scholar 

  48. Wang M, Sun C, Sowmya A (2022) Efficient corner detection based on corner enhancement filters. Digital Signal Process 122:103364

    Article  Google Scholar 

  49. Wu X, Jiang Y, Masaya K, Taniguchi T, Yamato T (2017) Study on the correlation of vibration properties and crack index in the health assessment of tunnel lining. Shock Vibration 2017

  50. Yue X, Wang J, Wang R, Geng Z (2021) A technology of invariant feature extraction of uav remote sensing image based on fuzzy fractional order function. Arabian J Geosci 14(18):1–13

    Article  Google Scholar 

  51. Zhang C, Sun M, Wei Y, Zhang H, Xie S, Liu T (2019) Automatic segmentation of arterial tree from 3d computed tomographic pulmonary angiography (ctpa) scans. Comput Assisted Surg 24(sup2):79–86

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Acknowledgements

José Francisco Gómez Aguilar acknowledges the support provided by CONACyT: Cátedras CONACyT para jóvenes investigadores 2014. José Francisco Gómez Aguilar, Jorge Enrique Lavín Delgado, and José Andrés Alanís Navarro acknowledges the support provided by SNI-CONACyT.

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Authors and Affiliations

  1. Universidad Politécnica del Estado de Guerrero (UPEGro), Dirección de Ingeniería en Redes y Telecomunicaciones, Puente Campuzano, Carretera Federal Iguala-Taxco K.M. 105, Municipio de Taxco de Alarcón, Guerrero, 40321, México

    J. E. Lavín-Delgado & D. E. Urueta-Hinojosa

  2. CONACyT-TecNM/CENIDET, Interior Internado Palmira S/N, Col. Palmira, Morelos, Cuernavaca, 62490, México

    J. F. Gómez-Aguilar

  3. Universidad La Salle. Facultad de Ingeniería, Bejamín Franklin No. 45, Col. Condesa, Ciudad de México, Alcaldía Cuautémoc, 06140, México

    Z. Zamudio-Beltrán

  4. Universidad Politécnica del Estado de Guerrero (UPEGro), Dirección de Ingeniería en Energía y Tecnología ambiental, Puente Campuzano, Carretera Federal Iguala-Taxco K.M. 105, Municipio de Taxco de Alarcón, Guerrero, 40321, México

    J. A. Alanís-Navarro

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  1. J. E. Lavín-Delgado
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All authors contributed equally and significantly in writing this article. All authors read and approved the final manuscript

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Correspondence to J. F. Gómez-Aguilar.

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Lavín-Delgado, J.E., Gómez-Aguilar, J.F., Urueta-Hinojosa, D.E. et al. An efficient technique for object recognition using fractional Harris–Stephens corner detection algorithm. Multimed Tools Appl 83, 23173–23199 (2024). https://doi.org/10.1007/s11042-023-16428-0

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