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Mahmmod, BM, Abdulhussain, SH, Naser, MA, Alsabah, M, Hussain, A and Al-Jumeily, D (2022) 3D Object Recognition Using Fast Overlapped Block Processing Technique. Sensors, 22 (23). ISSN 1424-8220
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3D Object Recognition Using Fast Overlapped Block Processing Technique.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Visual Perception; Algorithms; Image Processing, Computer-Assisted; Pattern Recognition, Automated; Support Vector Machine; 3D recognition; Charlier polynomials; SVM; features extraction; orthogonal moments; orthogonal polynomials; overlapped block processing; Pattern Recognition, Automated; Algorithms; Image Processing, Computer-Assisted; Support Vector Machine; Visual Perception; 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | MDPI AG |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 02 Feb 2023 16:13 |
| Last Modified: | 02 Feb 2023 16:15 |
| DOI or ID number: | 10.3390/s22239209 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18800 |
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