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Extension of Moment Features’ Invariance to Blur

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Abstract

Moment invariants are features calculated on an image, which do not change their values after a transformation of the image. This paper focuses on the so called combined invariants, which obey additional requirement of invariance to image blurring. Our first contribution is a review of achievements most relevant to the derivation of algebraic, moment and combined invariants. The review explains and develops parallels between the moment and the blur invariants. Gradually, it reveals new properties, simplifying construction of the combined invariants, but having more general extent. Resulting substitution rules for easy construction of the combined invariants from other invariants are thus the main results of this paper. All the conclusions can be understood without knowledge of the tensor calculus. This paper addresses construction of the combined invariants in arbitrary dimension.

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References

  1. Dudani, S.H., Breeding, K.J., McGhee, R.B.: Aircraft identification by moment invariants. IEEE Trans. Comput. 26(1), 39–45 (1977)

    Article  Google Scholar 

  2. Belkasim, S.O., Shridhar, M., Ahmadi, M.: Pattern recognition with moment invariants: a comparative study and new results. Pattern Recognit. 24(12), 1117–1138 (1991)

    Article  Google Scholar 

  3. Wong, R.Y., Hall, E.L.: Scene matching with invariant moments. Comput. Graph. Image Process. 8, 16–24 (1978)

    Google Scholar 

  4. Goshtasby, A.: Template matching in rotated images. IEEE Trans. Pattern Anal. Mach. Intell. 7, 338–344 (1985)

    Google Scholar 

  5. Flusser, J., Suk, T.: A moment-based approach to registration of images with affine geometric distortion. IEEE Trans. Geosci. Remote Sens. 32, 382–387 (1994)

    Article  Google Scholar 

  6. Mukundan, R., Ramakrishnan, K.R.: An iterative solution for object pose parameters using image moments. Pattern Recognit. Lett. 17, 1279–1284 (1996)

    Article  Google Scholar 

  7. Mukundan, R., Malik, N.K.: Attitude estimation using moment invariants. Pattern Recognit. Lett. 14, 199–205 (1993)

    Article  Google Scholar 

  8. Sluzek, A.: Identification and inspection of 2-D objects using new moment-based shape descriptors. Pattern Recognit. Lett. 16, 687–697 (1995)

    Article  Google Scholar 

  9. El-Khaly, F., Sid-Ahmed, M.A.: Machine recognition of optically captured machine printed arabic text. Pattern Recognit. 23, 1207–1214 (1990)

    Article  Google Scholar 

  10. Tsirikolias, K., Mertzios, B.G.: Statistical pattern recognition using efficient two-dimensional moments with applications to character recognition. Pattern Recognit. 26, 877–882 (1993)

    Article  Google Scholar 

  11. Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12, 489–497 (1990)

    Article  Google Scholar 

  12. Flusser, J., Suk, T.: Affine moment invariants: a new tool for character recognition. Pattern Recognit. Lett. 15, 433–436 (1994)

    Article  Google Scholar 

  13. Maitra, S.: Moment invariants. In: Proceedings of the IEEE, vol. 67, pp. 697–699 (1979)

  14. Hupkens, T.M., de Clippeleir, J.: Noise and intensity invariant moments. Pattern Recognit. 16, 371–376 (1995)

    Article  Google Scholar 

  15. Wang, L., Healey, G.: Using Zernike moments for the illumination and geometry invariant classification of multispectral texture. IEEE Trans. Image Process. 7, 196–203 (1998)

    Article  Google Scholar 

  16. Li, Y.: Reforming the theory of invariant moments for pattern recognition. Pattern Recognit. 25, 723–730 (1992)

    Article  Google Scholar 

  17. Wong, W.-H., Siu, W.-C., Lam, K.-M.: Generation of moment invariants and their uses for character recognition. Pattern Recognit. Lett. 16(2), 115–123 (1995)

    Article  Google Scholar 

  18. Rothe, I., Süsse, H., Voss, K.: The method of normalization to determine invariants. IEEE Trans. Pattern Anal. Mach. Intell. 18, 366–376 (1996)

    Article  Google Scholar 

  19. Shen, D., Ip, H.H.S.: Generalized affine invariant image normalization. IEEE Trans. Pattern Anal. Mach. Intell. 19, 431–440 (1997)

    Article  Google Scholar 

  20. Van Gool, L.J., Moons, T., Ungureanu, D.: Affine/photometric invariants for planar intensity patterns. In: ECCV’96: Proceedings of the 4th European Conference on Computer Vision, London, UK, vol. I, pp. 642–651. Springer, Berlin (1996)

    Google Scholar 

  21. Mindru, F., Moons, T., Van Gool, L.J.: Recognizing color patterns irrespective of viewpoint and illumination. In: CVPR’99: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. I, pp. 368–373 (1999)

  22. Flusser, J., Suk, T.: Degraded image analysis: an invariant approach. IEEE Trans. Pattern Anal. Mach. Intell. 20, 590–603 (1998)

    Article  Google Scholar 

  23. Flusser, J., Suk, T., Saic, S.: Recognition of blurred images by the method of moments. IEEE Trans. Image Process. 5, 533–538 (1996)

    Article  Google Scholar 

  24. Zhang, Y., Wen, C., Zhang, Y.: Estimation of motion parameters from blurred images. Pattern Recognit. Lett. 21, 425–433 (2000)

    Article  MathSciNet  Google Scholar 

  25. Zhang, Y., Wen, C., Zhang, Y., Soh, Y.C.: Determination of blur and affine combined invariants by normalization. Pattern Recognit. 35, 211–221 (2002)

    Article  MATH  Google Scholar 

  26. Lu, J., Yoshida, Y.: Blurred image recognition based on phase invariants. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E82A, 1450–1455 (1999)

    Google Scholar 

  27. Bentoutou, Y., Taleb, N., Mezouar, M., Taleb, M., Jetto, L.: An invariant approach for image registration in digital subtraction angiography. Pattern Recognit. 35, 2853–2865 (2002)

    Article  MATH  Google Scholar 

  28. Zhang, Y., Zhang, Y., Wen, C.: A new focus measure method using moments. Image Vis. Comput. 18, 959–965 (2000)

    Article  Google Scholar 

  29. Flusser, J., Suk, T., Saic, S.: Image features invariant with respect to blur. Pattern Recognit. 28, 1723–1732 (1995)

    Article  Google Scholar 

  30. Flusser, J., Suk, T., Saic, S.: Recognition of images degraded by linear motion blur without restoration. In: Proceedings of the 7th Workshop on Theoretical Foundations of Computer Vision, Computing Supplement, London, UK, pp. 37–51. Springer, Berlin (1996)

    Google Scholar 

  31. Stern, A., Kruchakov, I., Yoavi, E., Kopeika, S.: Recognition of motion-blured images by use of the method of moments. Appl. Opt. 41, 2164–2172 (2002)

    Article  Google Scholar 

  32. Flusser, J., Zitová, B.: Combined invariants to linear filtering and rotation. Int. J. Pattern Recognit. Artif. Intell. 13, 1123–1136 (1999)

    Article  Google Scholar 

  33. Suk, T., Flusser, J.: Combined blur and affine moment invariants and their use in pattern recognition. Pattern Recognit. 36, 2895–2907 (2003)

    Article  MATH  Google Scholar 

  34. Flusser, J., Zitová, B., Suk, T.: Invariant-based registration of rotated and blurred images. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 1262–1264. IEEE Computer Society, Los Alamitos (1999)

    Google Scholar 

  35. Zitová, B., Flusser, J.: Estimation of camera planar motion from blurred images. In: ICIP’02: Proceedings of the IEEE International Conference on Image Processing, vol. II, pp. 329–332 (2002)

  36. Flusser, J., Boldyš, J.: Registration of N-D images by blur invariants. In: Pernus, F. (ed.) Biomedical Image Registration, pp. 163–172. Slovenian Pattern Recognition Society, Ljubljana (1999)

    Google Scholar 

  37. Flusser, J., Boldyš, J., Zitová, B.: Invariants to convolution in arbitrary dimensions. J. Math. Imaging Vis. 13, 101–113 (2000)

    Article  MATH  Google Scholar 

  38. Flusser, J., Boldyš, J., Zitová, B.: Moment forms invariant to rotation and blur in arbitrary number of dimensions. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 234–246 (2003)

    Article  Google Scholar 

  39. Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)

    Article  Google Scholar 

  40. Hilbert, D.: Theory of Algebraic Invariants. Cambridge University Press, Cambridge (1993)

    MATH  Google Scholar 

  41. Gurevich, G.B.: Foundations of the Theory of Algebraic Invariants. Noordhoff, Groningen (1964)

    MATH  Google Scholar 

  42. Dirilten, H., Newman, T.G.: Pattern matching under affine transformations. IEEE Trans. Comput. 26, 314–317 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  43. Reiss, T.H.: Features invariant to linear transformations in 2D and 3D. In: Proceedings of the 11th IAPR International Conference on Pattern Recognit., Conference C: Image, Speech and Signal Analysis, vol. III, pp. 493–496 (1992)

  44. Markandey, V., deFigueiredo, R.J.P.: Robot sensing techniques based on high-dimensional moment invariants and tensors. IEEE Trans. Robot. Autom. 8, 186–195 (1992)

    Article  Google Scholar 

  45. Mamistvalov, A.G.: n-dimensional moment invariants and conceptual mathematical theory of recognition n-dimensional solids. IEEE Trans. Pattern Anal. Mach. Intell. 20, 819–831 (1998)

    Article  Google Scholar 

  46. Mamistvalov, A.G.: On the construction of affine invariants of n-dimensional patterns. Bull. Acad. Sci. Georgian SSR 76, 61–64 (1974)

    MATH  MathSciNet  Google Scholar 

  47. Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Pattern Recognit. 26, 167–174 (1993)

    Article  MathSciNet  Google Scholar 

  48. Reiss, T.H.: The revised fundamental theorem of moment invariants. IEEE Trans. Pattern Anal. Mach. Intell. 13, 830–834 (1991)

    Article  Google Scholar 

  49. Flusser, J.: On the independence of rotation moment invariants. Pattern Recognit. 33, 1405–1410 (2000)

    Article  Google Scholar 

  50. Suk, T., Flusser, J.: Graph method for generating affine moment invariants. In: ICPR’04: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 192–195. IEEE Computer Society, Los Alamitos (2004)

    Chapter  Google Scholar 

  51. Flusser, J., Zitová, B.: Combined invariants to linear filtering and rotation. Int. J. Pattern Recognit. Artif. Intell. 13(8), 1123–1136 (1999)

    Article  Google Scholar 

  52. Flusser, J., Zitová, B.: Invariants to convolution with circularly symmetric PSF. In: ICPR’04: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 11–14. IEEE Computer Society, Los Alamitos (2004)

    Chapter  Google Scholar 

  53. Lo, C.-H., Don, H.-S.: 3-D moment forms: their construction and application to object identification and positioning. IEEE Trans. Pattern Anal. Mach. Intell. 11, 1053–1064 (1989)

    Article  Google Scholar 

  54. Guo, X.: Three-dimensional moment invariants under rigid transformation. In: CAIP’93: Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns, London, UK, pp. 518–522. Springer, Berlin (1993)

    Google Scholar 

  55. Reiss, T.H.: Recognizing Planar Objects Using Invariant Image Features. Springer, Berlin (1993)

    MATH  Google Scholar 

  56. Suk, T., Flusser, J.: Affine normalization of symmetric objects. In: Proceedings of the 7th International Conference on Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, vol. 3708, pp. 100–107. Springer, Berlin (2005)

    Chapter  Google Scholar 

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Correspondence to Jiří Boldyš.

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Boldyš, J., Flusser, J. Extension of Moment Features’ Invariance to Blur. J Math Imaging Vis 32, 227–238 (2008). https://doi.org/10.1007/s10851-008-0091-4

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