caa a22 4500 606205586 CHVBK 20210128101007.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00034-014-9881-7 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9881-7 A Fast Computation of Novel Set of Meixner Invariant Moments for Image Analysis [Elektronische Daten] [Mhamed Sayyouri, Abdeslam Hmimid, Hassan Qjidaa] Recently, the discrete orthogonal moments have been introduced in image analysis and pattern recognition. They have better capacities than the continuous orthogonal moments but the use of these moments as feature descriptors is limited by their high computational cost. As a solution to this problem, we present in this paper an approach for fast computation of Meixner's discrete orthogonal moments. By using the recurrence relation with respect to variable x instead of order n in computation of Meixner's discrete orthogonal polynomials and the image block representation for binary images and intensity slice representation for gray-scale images. The acceleration of the computation time of Meixner moments is due to an innovative image representation where the image is described by a number of homogenous rectangular blocks instead of individual pixels. A novel set of invariant moments based on the Meixner discrete orthogonal moments is also proposed. These invariant moments are derived algebraically from the geometric invariant momenst and their computation is accelerated using image representation scheme. The presented algorithms are tested in several well-known computer vision datasets, regarding computational time, image reconstruction, invariability, and classification. The performances of Meixner invariant moments used as pattern features for a pattern classification application are compared with Hu, Tchebichef, dual-Hahn, and Krawtchouk invariant moments. Springer Science+Business Media New York, 2014 Meixner moments nationallicence Meixner invariant moments nationallicence Image block representation nationallicence Image reconstruction nationallicence Classification nationallicence Sayyouri Mhamed CED-ST, LESSI, Faculty of Sciences Dhar el mehraz, University Sidi Mohamed, Ben Abdellah, B.P., 1796, Fez-Atlas, Morocco aut Hmimid Abdeslam CED-ST, LESSI, Faculty of Sciences Dhar el mehraz, University Sidi Mohamed, Ben Abdellah, B.P., 1796, Fez-Atlas, Morocco aut Qjidaa Hassan CED-ST, LESSI, Faculty of Sciences Dhar el mehraz, University Sidi Mohamed, Ben Abdellah, B.P., 1796, Fez-Atlas, Morocco aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/3(2015-03-01), 875-900 0278-081X 34:3<875 2015 34 34 https://doi.org/10.1007/s00034-014-9881-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9881-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sayyouri Mhamed CED-ST, LESSI, Faculty of Sciences Dhar el mehraz, University Sidi Mohamed, Ben Abdellah, B.P., 1796, Fez-Atlas, Morocco aut NATIONALLICENCE 700 1- Hmimid Abdeslam CED-ST, LESSI, Faculty of Sciences Dhar el mehraz, University Sidi Mohamed, Ben Abdellah, B.P., 1796, Fez-Atlas, Morocco aut NATIONALLICENCE 700 1- Qjidaa Hassan CED-ST, LESSI, Faculty of Sciences Dhar el mehraz, University Sidi Mohamed, Ben Abdellah, B.P., 1796, Fez-Atlas, Morocco aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/3(2015-03-01), 875-900 0278-081X 34:3<875 2015 34 34