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Pattern Recognition and Image Analysis
Erschienen in: Pattern Recognition and Image Analysis 1/2020

01.01.2020 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING

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

verfasst von: Amal Zouhri, Hicham Amakdouf, Mostafa El Mallahi, Ahmed Tahiri, Zakia Lakhliai, Driss Chenouni, Hassan Qjidaa

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 1/2020

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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.
Vorheriger Artikel Jointly Image Annotation and Classification Based on Supervised Multi-Modal Hierarchical Semantic Model
Nächster Artikel Application of Image Processing in Detection of Bone Diseases Using X-rays

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Metadaten
Titel
Invariant Gaussian–Hermite Moments Based Neural Networks for 3D Object Classification
verfasst von
Amal Zouhri
Hicham Amakdouf
Mostafa El Mallahi
Ahmed Tahiri
Zakia Lakhliai
Driss Chenouni
Hassan Qjidaa
Publikationsdatum
01.01.2020
Verlag
Pleiades Publishing
Erschienen in
Pattern Recognition and Image Analysis / Ausgabe 1/2020
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI
https://doi.org/10.1134/S1054661820010186

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