Abstract
This paper describes a new method for 2-D shape recognition based on a multiresolution characterisation of the shape. From the Wavelet coefficients, features are extracted in order to perform a translation-rotation-scaling invariant recognition. Wavelets and multiresolution are exploited in order to reduce complexity of the matching task between the input image and the set of models. In the paper, motivations and performance of the algorithm are presented. Experimental results are also reported in several tests, including noise addition. The approach is quite general, and it could be extended to texture analysis, thus providing a unified paradigm for shape and texture recognition.
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References
A. Haar, Zur Theorie der orthogonalen Funktionensysteme, Math. Annal. 69, pp. 331–371, 1910.
Daubechies, Orthonormal Bases of Compactly Supported Wavelets, Communications on Pure and Applied Mathematics, XLI, pp 909–906, (1988).
S. Mallat, A Theory for Multiresolution Signal Decomposition: the Wavelet Representation.
Daubechies, The Wavelet Transform, Time-Frequency Localization and Signal Analysis, IEEE Trans. on Information Theory, 36, N. 5, pp 961–1005, (1990).
Vetterli, J. Kovacevic, Wavelets and subband coding, Prentice Hall, 1995.
S. Basu, C. Chiang, Wavelets and Perfect Reconstruction Subband Coding with Casual Stable IIR Filters, IEEE Trans. On Circuits and Systems-II: Analog Digital Signal Processing, vol. 42, N. 1, 1995.
S. Mallat, Multifrequencies Channel Decompositions of Images and Wavelets Models, IEEE Trans. On Acoustics, Speech and Signal Processing, Vol. 37, N. 12, 1989.
J. M. Shapiro, An Embedded hierarchical Image Coder using Zerotrees of Wavelets Coefficients, Proc. of DCC Conference, pp. 214–223, 1993.
M. V. Wickerauser, Acoustic signal compression using Walsh-type wave packets, Yale University, August 1989.
F. Argenti, B. Benelli, A. Mecocci, Source Coding and Transmission of HDTV Image Compression with the Wavelet Transform, IEEE Journal on Selected Areas in Communications, vol. 11, N. 1, 1993.
M. Lang, h. Guo, J. Odegard, C. S. Burrus, R.O. Wells, Nonlinear processing of a shift-invariant DWT for noise reduction, SPIE Conf. on Wavelet Applications, vol. 2491, Orlando, Florida, 1995.
F. Rué. A. Bijaoui, A Multiscale Vision Model Applied to Astronomical Images, Vistas in Astronomy, special issue on Vision Modelling and Information Coding, Vol. 40, N. 4, 1996, pp.502.
Y. Maday, V. Perrier, C. Ravel, Adaptivité dynamique sur bases d'ondeletts pour I'approximation d'équations aux dèrivèes patialles, C. R. acad. Sci. Paris, 312, Series I, pp. 45–41, 1991.
P. J. Burt, E. Adelson, The Laplacian pyramid as a compact image code, IEEE IEEE Trans. Communications, 31(4), pp. 532–540.
D. h. Ballard, N. C. Brown, Computer Vision, Prentice Hall Englewood Cliffs, NJ. 1982.
W. Wu, h. Mo, M. Sakauchi, An Image retrieving System Based on Userspecified Recognition model, MVA96, IAPR Works hop on Macine Vision Applications, Nov. 1996, pp. 506–531.
Y. Nakamura, T. Yoshida, Learning Two-Dimensional Shapes using Wavelets Local Extrema, Proc. of 12th IAPR International Conference on Pattern Recognition, Volume III, pp. 48–52, 1994
C. Arcelli, A. Ramella, Sketching a gray tone pattern from its distance transform, Pattern Recognition, Vol. 29, N. 12, 1996, pp. 2033–2045.
R. Leagult, C. Y. Suen, Contour tracing and parametric approximations of digitized patterns, A. Kryzak, T. Kasvand, C. Y. Seun eds., pp. 225–240, World Scientific Publishing, Singapore, 1989.
AA. VV. Dialgue.,CVGIM, pp. 65–118, 1994.
J. Alomonos, I Weiss, A. Bandyopadyay, Active Vision, Proc. First IEEE Int. Conference on Computer Vision, 35–54, London 1987.
ADV601p,Pm data sheet from Analog Devices
W. Lee, C.H. Kim, H. MA, Y.Y.Tang, Multiresolution recognitionof unconstrained handwritten numerals with Wavelet Transform and Multilayer Cluster Neural Network, Pattern recognition, Vol. 29, N. 12, pp. 1953–1961, 1996.
Cohen, I Daubechies, J. C. Feauveau, Biorthogonal bases of compactly supported Wavelets, Communications on Pure and Applied Mathematics, XLI, pp 485–560, (1992).
A. Watson, G. Yang, J. Solomon, J. Villasenor, Visual Thresholds for Wavelet Quantization Error, SPIE Porc. Vol. 2657, H uman Vision and Electronic Imaging, The Society for Imaging Science and Technology, 1996.
S. Bertoluzza, M. G. Albanesi, On the coupling of the Human Visual system and Wavelet transform for Image Compression, Proceedings of SPIE Mathematical Imaging: Wavelet Applications in Signal and Image Processing II, pp. 389–397, 1994.
H. Chao, P. Fisher, An Approach to Fast Integer Reversible Wavelet Transform for Image Compression, downloaded at ttp://www.mathsoft.com/wavelets.html
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© 1997 Springer-Verlag Berlin Heidelberg
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Albanesi, M.G., Lombardi, L. (1997). Wavelets for multiresolution shape recognition. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63508-4_133
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DOI: https://doi.org/10.1007/3-540-63508-4_133
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