Abstract
In this paper, we propose novel blur invariant features for the recognition of objects in images. The features are computed either using the phase-only spectrum or bispectrum of the images and are invariant to centrally symmetric blur, such as linear motion or defocus blur as well as linear illumination changes. The features based on the bispectrum are also invariant to translation, and according to our knowledge they are the only combined blur-translation invariants in the frequency domain. We have compared our features to the blur invariants based on image moments in simulated and real experiments. The results show that our features can recognize blurred images better and, in a practical situation, they are faster to compute using FFT.
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References
Wood, J.: Invariant pattern recognition: A review. Pattern Recognition 29, 1–17 (1996)
Banham, M.R., Katsaggelos, A.K.: Digital image restoration. IEEE Signal Processing Magazine 14(2), 24–41 (1997)
Kundur, D., Hatzinakos, D.: Blind image deconvolution. IEEE Signal Processing Magazine 13(3), 43–64 (1996)
Flusser, J., Suk, T.: Degraded image analysis: An invariant approach. IEEE Trans. Pattern Anal. Machine Intell. 20, 590–603 (1998)
Flusser, J., Suk, T., Saic, S.: Recognition of blurred images by the method of moments. IEEE Transactions on Image Processing 5(3), 533–538 (1996)
Bentoutou, Y., Taleb, N., Mezouar, M.C.E., Taleb, M., Jetto, L.: An invariant approach for image registration in digital subtraction angiography. Pattern Recognition 35, 2853–2865 (2002)
Flusser, J., Zitová, B.: Combined invariants to linear filtering and rotation. Int. J. Pattern Recognition and Artificial Intelligence 13(8), 1123–1136 (1999)
Zhang, Y., Wen, C., Zhang, Y., Soh, Y.C.: Determination of blur and affine combined invariants by normalization. Pattern recognition 35(1), 211–221 (2002)
Suk, T., Flusser, J.: Combined blur and affine moment invariants and their use in pattern recognition. Pattern Recognition 26(12), 2895–2907 (2003)
Ojansivu, V., Heikkilä, J.: Motion Blur Concealment of Digital Video Using Invariant Features. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 35–45. Springer, Heidelberg (2006)
Chandran, V., Carswell, B., Boashash, B., Elgar, S.: Pattern recognition using invariants defined from higher order spectra: 2-d image inputs. IEEE Transactions on Image Processing 6(5), 703–712 (1997)
Dianat, S.A., Rao, R.M.: Fast algorithms for phase and magnitude reconstruction from bispectra. Optical Engineering 29(5), 504–512 (1990)
Petropulu, A.P., Pozidis, H.: Phase reconstruction from bispectrum slices. IEEE Transactions on Signal Processing 46(2), 527–530 (1998)
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Ojansivu, V., Heikkilä, J. (2007). Object Recognition Using Frequency Domain Blur Invariant Features. In: Ersbøll, B.K., Pedersen, K.S. (eds) Image Analysis. SCIA 2007. Lecture Notes in Computer Science, vol 4522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73040-8_25
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DOI: https://doi.org/10.1007/978-3-540-73040-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73039-2
Online ISBN: 978-3-540-73040-8
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