Fast computation of 3D Tchebichef moments for higher orders

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.

show abstract

“…To compute the moments (

) for a 3D image using matrix multiplication, Equation ( 12 ) can be rewritten as follows [ 83 ]:

…”

Section: Methodology Of the Proposed Feature Extraction And Recogniti...mentioning

confidence: 99%

“…Finally, the objects are classified based on the extracted features. Algorithm 2 The 3D moments computation [ 83 ] Input:

= 3D image,

= Charlier polynomials. Output:

= Charlier moments.

…”

Section: Methodology Of the Proposed Feature Extraction And Recogniti...mentioning

confidence: 99%

3D Object Recognition Using Fast Overlapped Block Processing Technique

Mahmmod

Abdulhussain

Naser

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…This paper proposes a novel technique inspired by discrete orthogonal Hahn moments. Based on the literature, the three-term recurrence algorithms have been utilized in several existing works to tackle the problem of computational cost and propagation error due to gamma and binomial functions [ 35 ]. In [ 28 ], the n-direction recurrence algorithm was employed with an initial value starting at n, x = 0.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of high order Hahn polynomials using multithreading

Mahmmod,

Flayyih,

Fakhri

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Orthogonal polynomials and their moments have significant role in image processing and computer vision field. One of the polynomials is discrete Hahn polynomials (DHaPs), which are used for compression, and feature extraction. However, when the moment order becomes high, they suffer from numerical instability. This paper proposes a fast approach for computing the high orders DHaPs. This work takes advantage of the multithread for the calculation of Hahn polynomials coefficients. To take advantage of the available processing capabilities, independent calculations are divided among threads. The research provides a distribution method to achieve a more balanced processing burden among the threads. The proposed methods are tested for various values of DHaPs parameters, sizes, and different values of threads. In comparison to the unthreaded situation, the results demonstrate an improvement in the processing time which increases as the polynomial size increases, reaching its maximum of 5.8 in the case of polynomial size and order of 8000 × 8000 (matrix size). Furthermore, the trend of continuously raising the number of threads to enhance performance is inconsistent and becomes invalid at some point when the performance improvement falls below the maximum. The number of threads that achieve the highest improvement differs according to the size, being in the range of 8 to 16 threads in 1000 × 1000 matrix size, whereas at 8000 × 8000 case it ranges from 32 to 160 threads.

show abstract

A New Set of 3D Shifted Fractional-Order Gegenbauer Descriptors for Volumetric Image Representation

et al. 2022

View full text Add to dashboard Cite

Volumetric images have a three-dimensional (3D) view, in which viewers can examine their characteristics from any angle. The more accurate the digital representation of volumetric images, the more precise and valuable the assessment of what these images represent. The representation of volumetric images is a significant area of study in pattern recognition and computer vision. Recently, volumetric image analysis using orthogonal moments with fractional order has opened up a new study pathway, which has led scholars to discover many real-life applications through investigating efficient algorithms to represent the features of 3D images. In this study, a new set of 3D shifted fractional-order Gegenbauer moments (FrGMs) for volumetric image representation is proposed. First, a mathematical description of the shifted Gegenbauer moments for 3D images is presented. Second, a fast, highly accurate method for calculating the fractional-order shifted Gegenbauer moments of 3D images is introduced. Finally, the efficiency of the proposed FrGMs is evaluated through various suitable experiments and compared with existing methods in terms of the reconstruction of 3D images, the invariability property, sensitivity to noise, and computation time. The experimental results clearly show that FrGMs outperform existing related algorithms.

show abstract

Fast computation of 3D Tchebichef moments for higher orders

Cited by 3 publications

References 39 publications

3D Object Recognition Using Fast Overlapped Block Processing Technique

3D Object Recognition Using Fast Overlapped Block Processing Technique

Performance enhancement of high order Hahn polynomials using multithreading

A New Set of 3D Shifted Fractional-Order Gegenbauer Descriptors for Volumetric Image Representation

Contact Info

Product

Resources

About