Abstract
In this paper, we propose a practical parameter recovering approach, for similarity geometric transformations using only the Radon transform and its extended version on [0,2π]. The derived objective function is exploited as a similarity measure to perform an object recognition system. Comparison results with common and powerful shape descriptors testify the effectiveness of the proposed method in recognizing binary images, RST transformed, distorted, occluded or noised.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)
Tabbone, S., Wendling, L., Salmon, J.-P.: A new shape descriptor defined on the Radon transform. Computer Vision and Image Understanding 102, 42–51 (2006)
Wang, X., Xiao, B., Ma, J.-F., Bi, X.-L.: Scaling and rotation invariant analysis approach to object recognition based on Radon and Fourier-Mellin transforms. Pattern Recognition 40(12), 3503–3508 (2007)
Chen, Y.W., Chen, Y.Q.: Invariant Description and Retrieval of Planar Shapes using Radon Composite Features. IEEE Trans. on Signal Processing 56(10), 4762–4771 (2008)
Nacereddine, N., Tabbone, S., Ziou, D., Hamami, L.: Shape-based image retrieval using a new descriptor based on the Radon and wavelet transforms. In: 20th Intern. Conf. on Pattern Recognition, pp. 1997–2000 (2010)
Hjouj, F., Kammler, D.W.: Identification of Reflected, Scaled, Translated, and Rotated Objects from their Radon Projections. IEEE Trans. on Image Processing 17(3), 301–310 (2008)
Wan, Y., Wei, N.: A Fast Algorithm for Recognizing Translated, Rotated, Reflected, and Scaled Objects from only their Projections. IEEE Signal Processing Letters 17(1), 71–74 (2010)
Deans, S.R.: The Radon Transform and Some of its Applications. John Wiley & Sons, New York (1983)
Shepp, L.A., Logan, B.F.: Fourier reconstruction of a head section. IEEE Trans. Nuclear Sci. 21(3), 21–44 (1974)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing Shock Graphs. In: 8th Intern. Conference on Computer Vision, pp. 755–762 (2001)
Doermann, D.S., Rivlin, E., Weiss, I.: Applying algebraic and differential invariants for logo recognition. Machine Vision and Applications 9(2), 73–86 (1996)
Teh, C.H., Chin, R.T.: On image analysis by the method of moments. IEEE Trans. on Pattern Analysis and Machine Intelligence 10(4), 496–513 (1988)
Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. on Patt. Anal. and Mach. Intell. 12(5), 489–497 (1990)
Zhang, D., Lu, G.: Shape-based image retrieval using generic Fourier descriptor. Signal Processing: Image Communication 17(10), 825–848 (2002)
Jafari-Khouzani, K., Soltanian-Zadeh, H.: Rotation-invariant multiresolution texture analysis using Radon an wavelet transforms. IEEE Trans. on Image Processing 14(6), 783–795 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nacereddine, N., Tabbone, S., Ziou, D. (2012). Object Recognition Using Radon Transform-Based RST Parameter Estimation. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-33140-4_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33139-8
Online ISBN: 978-3-642-33140-4
eBook Packages: Computer ScienceComputer Science (R0)