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S.G. Mallat, "A Theory for Multiresolution Signal Decomposition: The Wavelet Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674693, July, 1989.  
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@article{ 10.1109/34.192463, author = {S.G. Mallat}, title = {A Theory for Multiresolution Signal Decomposition: The Wavelet Representation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {11}, number = {7}, issn = {01628828}, year = {1989}, pages = {674693}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.192463}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation IS  7 SN  01628828 SP674 EP693 EPD  674693 A1  S.G. Mallat, PY  1989 KW  picture processing; encoding; pattern recognition; multiresolution signal decomposition; wavelet representation; pyramidal algorithm; convolutions; quadrature mirror filters; data compression; image coding; texture discrimination; fractal analysis; data compression; encoding; pattern recognition; picture processing VL  11 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, squareintegrable ndimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed.
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