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S. Mallat, S. Zhong, "Characterization of Signals from Multiscale Edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 7, pp. 710732, July, 1992.  
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@article{ 10.1109/34.142909, author = {S. Mallat and S. Zhong}, title = {Characterization of Signals from Multiscale Edges}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {7}, issn = {01628828}, year = {1992}, pages = {710732}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.142909}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Characterization of Signals from Multiscale Edges IS  7 SN  01628828 SP710 EP732 EPD  710732 A1  S. Mallat, A1  S. Zhong, PY  1992 KW  1D signals; 2D signals; picture processing; multiscale Canny edge detection; local maxima; wavelet theory; pattern recognition; multiscale edge representation; image coding; pattern recognition; picture processing VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A multiscale Canny edge detection is equivalent to finding the local maxima of a wavelet transform. The authors study the properties of multiscale edges through the wavelet theory. For pattern recognition, one often needs to discriminate different types of edges. They show that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures. Numerical descriptors of edge types are derived. The completeness of a multiscale edge representation is also studied. The authors describe an algorithm that reconstructs a close approximation of 1D and 2D signals from their multiscale edges. For images, the reconstruction errors are below visual sensitivity. As an application, a compact image coding algorithm that selects important edges and compresses the image data by factors over 30 has been implemented.
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