{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T08:57:48Z","timestamp":1742806668741,"version":"3.37.3"},"reference-count":86,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T00:00:00Z","timestamp":1669420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"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.<\/jats:p>","DOI":"10.3390\/s22239209","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T13:13:09Z","timestamp":1669641189000},"page":"9209","source":"Crossref","is-referenced-by-count":12,"title":["3D Object Recognition Using Fast Overlapped Block Processing Technique"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4121-0843","authenticated-orcid":false,"given":"Basheera M.","family":"Mahmmod","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6439-0082","authenticated-orcid":false,"given":"Sadiq H.","family":"Abdulhussain","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq"}]},{"given":"Marwah Abdulrazzaq","family":"Naser","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4ET, UK"}]},{"given":"Muntadher","family":"Alsabah","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4ET, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8413-0045","authenticated-orcid":false,"given":"Abir","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK"},{"name":"Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates"}]},{"given":"Dhiya","family":"Al-Jumeily","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"148191","DOI":"10.1109\/ACCESS.2021.3124812","article-title":"6G wireless communications networks: A comprehensive survey","volume":"9","author":"Alsabah","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65091","DOI":"10.1109\/ACCESS.2021.3076359","article-title":"DeepWTPCA-L1: A new deep face recognition model based on WTPCA-L1 norm features","volume":"9","author":"Maafiri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1504\/IJCAT.2011.038559","article-title":"Partially occluded object recognition","volume":"40","author":"Lim","year":"2011","journal-title":"Int. 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