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F. Chin, A. Choi, Y. Luo, "Optimal Generating Kernels for Image Pyramids by Piecewise Fitting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 12, pp. 11901198, December, 1992.  
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@article{ 10.1109/34.177384, author = {F. Chin and A. Choi and Y. Luo}, title = {Optimal Generating Kernels for Image Pyramids by Piecewise Fitting}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {12}, issn = {01628828}, year = {1992}, pages = {11901198}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.177384}, 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  Optimal Generating Kernels for Image Pyramids by Piecewise Fitting IS  12 SN  01628828 SP1190 EP1198 EPD  11901198 A1  F. Chin, A1  A. Choi, A1  Y. Luo, PY  1992 KW  optimal generating kernels; convolution; mean square error minimization; image pyramids; piecewise fitting; intensity functions; continuous piecewise surfaces; polynomial tensor products; symmetry; normalization; unimodality; equal contribution properties; small window size; fast inverse transformation; minimum error; image processing; least squares approximations; optimisation VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A novel class of generating kernels for image pyramids is introduced. When these kernels are convolved with intensity functions of images, continuous piecewise surfaces composed of polynomial tensor products are fitted to the intensity functions. The fittings are optimal in the sense that the mean square error between them and the original intensity functions is minimized. Two members of the class are introduced, and symmetry, normalization, unimodality, and equal contribution properties are proved. These kernels possess attractive properties such as small window size, fast inverse transformation, and minimum error. Experiments show that they compare favorably with existing ones in terms of mean square error.
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