%0 Journal Article %1 Papakostas2007 %A Papakostas, George A. %A Boutalis, Yiannis S. %A Karras, Dimitris A. %A Mertzios, Basil G. %D 2007 %J Inf. Sci. %K Direct Pattern Stirling's Zernike approximation classification; method; moments; %N 13 %P 2802-2819 %T A new class of Zernike moments for computer vision applications %V 177 %X A Modified Direct Method for the computation of the Zernike moments is presented in this paper. The presence of many factorial terms, in the direct method for computing the Zernike moments, makes their computation process a very time consuming task. Although the computational power of the modern computers is impressively increasing, the calculation of the factorial of a big number is still an inaccurate numerical procedure. The main concept of the present paper is that, by using Stirling's Approximation formula for the factorial and by applying some suitable mathematical properties, a novel, factorial-free direct method can be developed. The resulted moments are not equal to those computed by the original direct method, but they are a sufficiently accurate approximation of them. Besides, their variability does not affect their ability to describe uniquely and distinguish the objects they represent. This is verified by pattern recognition simulation examples.

@article{Papakostas2007, abstract = {A Modified Direct Method for the computation of the Zernike moments is presented in this paper. The presence of many factorial terms, in the direct method for computing the Zernike moments, makes their computation process a very time consuming task. Although the computational power of the modern computers is impressively increasing, the calculation of the factorial of a big number is still an inaccurate numerical procedure. The main concept of the present paper is that, by using Stirling's Approximation formula for the factorial and by applying some suitable mathematical properties, a novel, factorial-free direct method can be developed. The resulted moments are not equal to those computed by the original direct method, but they are a sufficiently accurate approximation of them. Besides, their variability does not affect their ability to describe uniquely and distinguish the objects they represent. This is verified by pattern recognition simulation examples.}, added-at = {2011-03-27T19:47:06.000+0200}, author = {Papakostas, George A. and Boutalis, Yiannis S. and Karras, Dimitris A. and Mertzios, Basil G.}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/27a25811931705646e60a7eff1e2193bf/cocus}, ee = {http://dx.doi.org/10.1016/j.ins.2007.01.010}, file = {:./papakostaszernikeapprox.pdf:PDF}, interhash = {f9c1555198ccc0ed717641286dcc3ea3}, intrahash = {7a25811931705646e60a7eff1e2193bf}, journal = {Inf. Sci.}, keywords = {Direct Pattern Stirling's Zernike approximation classification; method; moments;}, number = 13, pages = {2802-2819}, timestamp = {2011-03-27T19:47:09.000+0200}, title = {A new class of Zernike moments for computer vision applications}, volume = 177, year = 2007 }