Bendlet transform based object detection system using proximity learning approach
This study presents a Bendlet Transform-based Object Detection (BTOD) system that recognizes an object in the image. Finding a specific object in images or videos is the goal of the field of object recognition. Though humans are able to identify a large number of objects, it is very difficult for co...
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| Format: | Article |
| Language: | en |
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XLESCIENCE
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44890/1/Bendlet%20transform%20based%20object%20detection%20system%20using%20proximity.pdf http://umpir.ump.edu.my/id/eprint/44890/ https://doi.org/10.29284/ijasis.8.2.2022.1-8 |
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| _version_ | 1839752497349001216 |
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| author | Ramalingam, Mritha Nishanthi, C.H. |
| author_facet | Ramalingam, Mritha Nishanthi, C.H. |
| author_sort | Ramalingam, Mritha |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This study presents a Bendlet Transform-based Object Detection (BTOD) system that recognizes an object in the image. Finding a specific object in images or videos is the goal of the field of object recognition. Though humans are able to identify a large number of objects, it is very difficult for computer vision systems in general. The appearance of the objects may change depending on the perspective, the size or scale, or translation and rotation. This work extracts Bendlet transform-based features from the images at different levels, and then the discriminant features are selected by employing genetic algorithms. The performance of the BTOD system is analyzed with different nearest neighbours for classifying objects in the Columbia Object Image Library (COIL-100) in terms of classification accuracy. It is observed from the results that the BTOD system with a one-nearest neighbour provides better performance than the two-nearest neighbour classifier. The former classifier gives 99.47% accuracy, whereas the later classifier gives 99.19%. |
| format | Article |
| id | my.ump.umpir-44890 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2022 |
| publisher | XLESCIENCE |
| record_format | eprints |
| spelling | my.ump.umpir-448902025-08-05T04:00:02Z http://umpir.ump.edu.my/id/eprint/44890/ Bendlet transform based object detection system using proximity learning approach Ramalingam, Mritha Nishanthi, C.H. QA Mathematics TK Electrical engineering. Electronics Nuclear engineering This study presents a Bendlet Transform-based Object Detection (BTOD) system that recognizes an object in the image. Finding a specific object in images or videos is the goal of the field of object recognition. Though humans are able to identify a large number of objects, it is very difficult for computer vision systems in general. The appearance of the objects may change depending on the perspective, the size or scale, or translation and rotation. This work extracts Bendlet transform-based features from the images at different levels, and then the discriminant features are selected by employing genetic algorithms. The performance of the BTOD system is analyzed with different nearest neighbours for classifying objects in the Columbia Object Image Library (COIL-100) in terms of classification accuracy. It is observed from the results that the BTOD system with a one-nearest neighbour provides better performance than the two-nearest neighbour classifier. The former classifier gives 99.47% accuracy, whereas the later classifier gives 99.19%. XLESCIENCE 2022 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/44890/1/Bendlet%20transform%20based%20object%20detection%20system%20using%20proximity.pdf Ramalingam, Mritha and Nishanthi, C.H. (2022) Bendlet transform based object detection system using proximity learning approach. International Journal Of Advances In Signal And Image Sciences, 8 (2). pp. 1-8. ISSN 2457-0370. (Published) https://doi.org/10.29284/ijasis.8.2.2022.1-8 https://doi.org/10.29284/ijasis.8.2.2022.1-8 |
| spellingShingle | QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Ramalingam, Mritha Nishanthi, C.H. Bendlet transform based object detection system using proximity learning approach |
| title | Bendlet transform based object detection system using proximity learning approach |
| title_full | Bendlet transform based object detection system using proximity learning approach |
| title_fullStr | Bendlet transform based object detection system using proximity learning approach |
| title_full_unstemmed | Bendlet transform based object detection system using proximity learning approach |
| title_short | Bendlet transform based object detection system using proximity learning approach |
| title_sort | bendlet transform based object detection system using proximity learning approach |
| topic | QA Mathematics TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44890/1/Bendlet%20transform%20based%20object%20detection%20system%20using%20proximity.pdf http://umpir.ump.edu.my/id/eprint/44890/ https://doi.org/10.29284/ijasis.8.2.2022.1-8 https://doi.org/10.29284/ijasis.8.2.2022.1-8 |
| url_provider | http://umpir.ump.edu.my/ |