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01-01-2020 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING | Issue 1/2020

Pattern Recognition and Image Analysis 1/2020

Invariant Gaussian–Hermite Moments Based Neural Networks for 3D Object Classification

Journal:
Pattern Recognition and Image Analysis > Issue 1/2020
Authors:
Amal Zouhri, Hicham Amakdouf, Mostafa El Mallahi, Ahmed Tahiri, Zakia Lakhliai, Driss Chenouni, Hassan Qjidaa
Important notes
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Amal Zouhri received the B.Sc., M.Sc., and PhD degrees in electrical engineering from Faculty of Sciences, Sidi Mohammed Ben Abdellah University, Morocco in 2008, 2011, and 2017, respectively. Her research interests include embedded system, stability and stabilization of interconnected systems, decentralized systems.
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Hicham Amakdouf received the B.Sc., M.Sc. degrees in computer sciences from Faculty of Sciences, Sidi Mohammed Ben Abdellah University, Morocco in 2003 and 2007, respectively. He is presently a PhD degree candidate in computer science at the Faculty of Sciences, Sidi Mohammed Ben Abdellah University, Morocco. His research interests include image processing, computer graphics, artificial intelligence, and geographic information systems.
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Mostafa El Mallahi received the B.Sc., M.Sc., and PhD degrees in computer science from Faculty of Sciences, University Sidi Mohammed Ben Abdellah, Morocco in 2000, 2007, and 2017, respectively. His research interests include image processing, pattern classification, orthogonal systems, neural networks, big data, data mining, data science, deep learning, genetic algorithms, and special functions.
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Ahmed Tahiri received the graduate degree DES in Department of Physics, Sidi Mohammed Ben Abdellah University, Morocco in 1997, received the PhD degree in physics and environment from the University Sidi Mohamed Ben Abdellah, Faculty of Science, Morocco in 2005. He completed his doctoral studies in didactics of science in the University of Sherbrooke in Canada in 2009. He is now a professor in Superior Normal School, ENS-FEZ, Morocco. His research interests include image processing, didactics of scientific disciplines, and environmental education.
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Zakia Lakhliai rеcеivеd thе PhD dеgrее in physics from thе Univеrsity of Montpеlliеr II, Francе, in 1987, and thе Statе Doctor’s dеgrее from thе Univеrsity of Fеs, Morocco, in 1996. Shе is currently a mеmbеr of Lab of computering and intеrdisciplinary physics (L.I.P.I.), at (Е.N.S.F.), and a Professor in thе Superior School of technology (Е.S.T.F.) at Sidi Mohammed Ben Abdellah Univеrsity (USMBA), Fez, Morocco. Hеr current research intеrеsts include signal and imagе procеssing, indеxation of old manuscripts.
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Driss Chenouni received the PhD degree in physics from the University of Montpellier II, France, in 1989, and the State Doctor’s degree from the University of Fes, Morocco, in 1996. He is currently a Lab director of computering and interdisciplinary physics (L.I.P.I.), at (E.N.S.F.), and a Director of the Ecole Normale Supérieur at Sidi Mohammed Ben Abdellah University (USMBA), Fez, Morocco. His current research interests include multi-agent systems, enterprise architecture, modeling, Web services, autonomic computing, image processing, and indexation of old manuscripts.
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Hassan Qjidaa received the M.Sc. and PhD degrees in physics from Claud Bernard University of Lyon, France in 1983 and 1987, respectively. He is a full professor of electrical engineering at the Faculty of Sciences, Sidi Mohammed Ben Abdellah University, Morocco 1999. He is now a professor in Sidi Mohammed Ben Abdellah University, Morocco. His research interests include classification, image processing, pattern recognition, orthogonal systems, neural networks, deep learning and data science, big data, genetic algorithms, and special functions.

Abstract

In this article, we suggest a new approach for classification and Recognition of 3D image Gaussian–Hermite moments using a Multilayer Perceptron architecture. The Multilayer Perceptron is an artificial neural network to evaluate the efficient structure in the non-linear systems. However, the determination of its architecture and weights is a fundamental issue due to their direct impact on the network convergence and performance. The robustness of the proposed approach have provided under many transforms. The experimental results show that our approaches are more robust than 3D Geometric moments.

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