Deformable 3D Shape Classification Using 3D Racah Moments and Deep Neural Networks

Lakhili, Zouhir; Alami, Abdelmajid El; Mesbah, Abderrahim; Berrahou, Aissam; Qjidaa, Hassan

doi:10.1016/j.procs.2019.01.002

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…El Mallahi et al in [32] used polar coordinates for derivation of 2D and 3D rotation invariants. Lakhili et al applied neural network on 3D Racah moments computed in Cartesian coordinates in [33]. In [34], Batioua et al combine Racah polynomials with other types.…”

Section: Introductionmentioning

confidence: 99%

Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

Mahmmod,

Abdulhussain,

Suk

et al. 2023

IEEE Access

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Discrete Racah polynomials (DRPs) are highly efficient orthogonal polynomials and used in various scientific fields for signal representation. They find applications in disciplines like image processing and computer vision. Racah polynomials were originally introduced by Wilson and later modified by Zhu to be orthogonal on a discrete set of samples. However, when the degree of the polynomial is high, it encounters numerical instability issues. In this paper, we propose a novel algorithm called Improved Stabilization (ImSt) for computing DRP coefficients. The algorithm partitions the DRP plane into asymmetric parts based on the polynomial size and DRP parameters. We have optimized the use of stabilizing conditions in these partitions. To compute the initial values, we employ the logarithmic gamma function along with a new formula. This combination enables us to compute the initial values efficiently for a wide range of DRP parameter values and large polynomial sizes. Additionally, we have derived a symmetry relation for the case when the Racah polynomial parameters are zero (a = 0, α = 0, β = 0). This symmetry makes the Racah polynomials symmetric about the main diagonal, and we present a different algorithm for this specific scenario. We have demonstrated that the ImSt algorithm works for a broader range of parameters and higher degrees compared to existing algorithms. A comprehensive comparison between ImSt and the existing algorithms has been conducted, considering the maximum polynomial degree, computation time, restriction error analysis, and reconstruction error. The results of the comparison indicate that ImSt outperforms the existing algorithms for various values of Racah polynomial parameters.

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Section: Introductionmentioning

confidence: 99%

Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

Mahmmod,

Abdulhussain,

Suk

et al. 2023

IEEE Access

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“…The use of Artificial Neural Networks for the classification and recognition of image patterns in applications involving process automation has been increasingly widespread and in different areas. Regarding the architectures and algorithms of ANNs applied in digital image processing, recent works describe the use of feed forward networks to extract features such as image textures (Tuncer et al, 2019), and the use of Deep Convolutional Neural Networks to classify objects in 3D images and real-time identification of objects in video images or advanced automation applications (Lakhili et al, 2018;Newby et al, 2018;Sharma et al, 2018;Wei et al, 2021). Computer vision problems are characterized by the large amount of manipulated information, which require the treatment of high definition images with a large amount of additional information, such as coloring and positioning in three-dimensional space (Braga et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Image processing for positioning mechanical device with Backpropagation algorithm and separate handling of RGB components

Mário

Oliveira

Filho

et al. 2022

RSD

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Different approaches for the use of Artificial Neural Networks - ANNs, in the recognition of image patterns, have been used with variations ranging from the processing of the image data to the ANN architecture itself. This paper describes the development of a system that aims to recognize patterns of images with ANNs of three inputs that receive images decomposed into their RGB components. The ANNs have an architecture with two hidden layers of six neurons each, and use the algorithm Backpropagation. The built model normalizes RGB components with values between zero and one. The Backpropagation algorithm is used for the purpose of functional approximation of these components, and after training, the numerical arrangements obtained in the three outputs corresponding to the inputs are denormalized to form the resulting training image. Six image pattern had training in different ANNs, forming a system to recognized each pattern. The feasibility of using the model was verified with the tests for its generalization capacity. Images used to position a mechanical device, which did not participate in the training, were inserted into the system and from them the positioning of the device was performed, with a high degree of accuracy.

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“…A multi-layer artificial neural network (ANN) perception approach was proposed in [ 23 ] for the classification and recognition of 3D images. In [ 24 ], a deep learning approach based on neural network and Racah-based moments was proposed for 3D shape classification. Additionally, in [ 16 ], an approach based on the combination of 3D discrete orthogonal moments and deep neural network (DNN) algorithms was proposed to improve the classification accuracy of the 3D object features.…”

Section: Introductionmentioning

confidence: 99%

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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Deformable 3D Shape Classification Using 3D Racah Moments and Deep Neural Networks

Cited by 12 publications

References 14 publications

Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

Image processing for positioning mechanical device with Backpropagation algorithm and separate handling of RGB components

3D Object Recognition Using Fast Overlapped Block Processing Technique

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