Robust Corner Detection Using Local Extrema Differences
Corner detection, crucial for many computer vision tasks due to corner's distinct structural properties, often relies on traditional intensity-based detectors developed before 2000. This paper introduces a novel intensity-based corner detector that surpasses existing methods by solely analyzing...
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| Format: | Article |
| Language: | English |
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University of science and culture
2024-01-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_200313_e8e2ef03ef36eb3261087bf4d7811d3f.pdf |
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| _version_ | 1846110262972645376 |
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| author | Reza Yazdi Hassan Khotanlou Hosna Khademfar |
| author_facet | Reza Yazdi Hassan Khotanlou Hosna Khademfar |
| author_sort | Reza Yazdi |
| collection | DOAJ |
| description | Corner detection, crucial for many computer vision tasks due to corner's distinct structural properties, often relies on traditional intensity-based detectors developed before 2000. This paper introduces a novel intensity-based corner detector that surpasses existing methods by solely analyzing pixel intensity within a 3×3 neighborhood. Our approach leverages a unique corner response function derived from intensity sorting and difference calculations. We conduct a comprehensive evaluation comparing our detector to seven established algorithms using five benchmark images with ground truth corner locations. The evaluation encompasses detection accuracy, localization error under varying noise levels, and repeatability under transformations and degradations. This assessment utilizes 28 diverse images without ground truth data. Experimental results demonstrate the proposed detector's superior overall performance by 3%. It achieves better accuracy in corner localization and reduces both missed detections and false positives. Furthermore, requiring only one parameter for adjustment, it offers computational efficiency and real-time processing potential. Additionally, the generated corner response map holds promise for integration with deep learning architectures, opening possibilities for further exploration. |
| format | Article |
| id | doaj-art-9311cb12e1c846c5ac3be42b10c6b0f3 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-9311cb12e1c846c5ac3be42b10c6b0f32024-12-24T12:02:40ZengUniversity of science and cultureInternational Journal of Web Research2645-43432024-01-0171698410.22133/ijwr.2024.458246.1217Robust Corner Detection Using Local Extrema DifferencesReza Yazdi0Hassan Khotanlou1Hosna Khademfar2RIV Lab, Computer Engineering, Bu-Ali Sina University, Hamadan, IranRIV Lab, Computer Engineering dept., Bu-Ali Sina University, Hamadan, Irandept. of Artificial intelligence, Shargh Golestan higher education institute, Golestan, IranCorner detection, crucial for many computer vision tasks due to corner's distinct structural properties, often relies on traditional intensity-based detectors developed before 2000. This paper introduces a novel intensity-based corner detector that surpasses existing methods by solely analyzing pixel intensity within a 3×3 neighborhood. Our approach leverages a unique corner response function derived from intensity sorting and difference calculations. We conduct a comprehensive evaluation comparing our detector to seven established algorithms using five benchmark images with ground truth corner locations. The evaluation encompasses detection accuracy, localization error under varying noise levels, and repeatability under transformations and degradations. This assessment utilizes 28 diverse images without ground truth data. Experimental results demonstrate the proposed detector's superior overall performance by 3%. It achieves better accuracy in corner localization and reduces both missed detections and false positives. Furthermore, requiring only one parameter for adjustment, it offers computational efficiency and real-time processing potential. Additionally, the generated corner response map holds promise for integration with deep learning architectures, opening possibilities for further exploration.https://ijwr.usc.ac.ir/article_200313_e8e2ef03ef36eb3261087bf4d7811d3f.pdfcorner points detectioncorner detectioninterested pointscorner points |
| spellingShingle | Reza Yazdi Hassan Khotanlou Hosna Khademfar Robust Corner Detection Using Local Extrema Differences International Journal of Web Research corner points detection corner detection interested points corner points |
| title | Robust Corner Detection Using Local Extrema Differences |
| title_full | Robust Corner Detection Using Local Extrema Differences |
| title_fullStr | Robust Corner Detection Using Local Extrema Differences |
| title_full_unstemmed | Robust Corner Detection Using Local Extrema Differences |
| title_short | Robust Corner Detection Using Local Extrema Differences |
| title_sort | robust corner detection using local extrema differences |
| topic | corner points detection corner detection interested points corner points |
| url | https://ijwr.usc.ac.ir/article_200313_e8e2ef03ef36eb3261087bf4d7811d3f.pdf |
| work_keys_str_mv | AT rezayazdi robustcornerdetectionusinglocalextremadifferences AT hassankhotanlou robustcornerdetectionusinglocalextremadifferences AT hosnakhademfar robustcornerdetectionusinglocalextremadifferences |