Design of Multi-Dimensional Derivative Filters

@inproceedings{citeulike:2629622, abstract = {Many multi-dimensional signal processing problems require the computation of signal gradients or directional derivatives. Traditional derivative estimates based on adjacent or central dierences are often inappropriate for multi-dimensional problems. As replacements for these traditional operators, we design a set of matched pairs of derivative lters and lowpass pre- lters. We demonstrate the superiority of these lters over simple dierence operators.}, author = {Simoncelli, Eero P. }, booktitle = {ICIP (1)}, citeulike-article-id = {2629622}, keywords = {discrete-differentiation, filters}, pages = {790--794}, posted-at = {2008-04-04 14:14:54}, priority = {2}, title = {Design of Multi-Dimensional Derivative Filters}, url = {http://citeseer.ist.psu.edu/simoncelli94design.html}, year = {1994} }

TY - CONF ID - citeulike:2629622 L3 - citeulike-article-id:2629622 TI - Design of Multi-Dimensional Derivative Filters T2 - ICIP (1) SP - 790 EP - 794 N2 - Many multi-dimensional signal processing problems require the computation of signal gradients or directional derivatives. Traditional derivative estimates based on adjacent or central dierences are often inappropriate for multi-dimensional problems. As replacements for these traditional operators, we design a set of matched pairs of derivative lters and lowpass pre- lters. We demonstrate the superiority of these lters over simple dierence operators. KW - discrete-differentiation KW - filters AU - Simoncelli, Eero PY - 1994/// UR - http://citeseer.ist.psu.edu/simoncelli94design.html ER -