@inproceedings{Urban2007_6,<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Abstract = {The cellular Yule-Nielsen modified spectral Neugebauer (CYNSN) model is widely used to predict the spectral response of inkjet printers. Inversion of the model is often implemented as a constrained optimization problem minimizing the difference between a desired reflectance and a model-predicted one. Inversion of CYNSN is considered to be computationally expensive. The Linear Regression Iteration (LRI) algorithm, already effectively used for the inversion of the simple YNSN model, is investigated as an efficient optimization method for inverting CYNSN. The proposed approach introduces a novel means for transitioning optimized choices from one cell to the neighboring cell. These transitions ensure the global consistency of the results. The matrices used by the LRI method can be calculated in advance for each cell and then arranged in a special scheme to allow fast access. This precalculated matrix scheme vastly increases the speed of inversion by reducing the algorithm to two simple steps for each  iteration: choice of correct matrix and two simple matrix-vector multiplications.},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Address = {Taipei, Taiwan},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Author = {Philipp Urban and Mitchell R.  Rosen},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Booktitle = {, Ninth International Symposium on Multispectral Color Science and Application},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Keywords = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Month = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Organization = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Pages = {29--35},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Title = {Inverting the Cellular Yule-Nielsen modified Spectral Neugebauer Model},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Url = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Volume = {},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Year = {2007}