3D Object Classification using 3D Racah Moments Convolutional Neural Networks

Mesbah, Abderrahim; Berrahou, Aissam; Alami, Abdelmajid El; Berrahou, Nadia; Berbia, Hassan; Qjidaa, Hassan

doi:10.1145/3320326.3320397

Cited by 7 publications

(2 citation statements)

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“…Besides, an automatic algorithm was proposed in [ 20 , 21 ] to generate 3D rotation invariants from geometric moments. Recently, a 3D Hahn moments combined with convolutional neural networks (CNN) was proposed in [ 22 ] to enhance the 3D object classification. Specifically, the work in [ 22 ] proposed a hybrid approach based on combining the 3D discrete Hahn moments and CNN to improve 3D object classification.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a 3D Hahn moments combined with convolutional neural networks (CNN) was proposed in [ 22 ] to enhance the 3D object classification. Specifically, the work in [ 22 ] proposed a hybrid approach based on combining the 3D discrete Hahn moments and CNN to improve 3D object classification. A multi-layer artificial neural network (ANN) perception approach was proposed in [ 23 ] for the classification and recognition of 3D images.…”

Section: Introductionmentioning

confidence: 99%

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3D Object Recognition Using Fast Overlapped Block Processing Technique

Mahmmod

Abdulhussain

Naser

et al. 2022

Sensors

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Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments.

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Section: Introductionmentioning

confidence: 99%