LV-FeatEx: Large Viewpoint-Image Feature Extraction
Maintaining stable image feature extraction under viewpoint changes is challenging, particularly when the angle between the camera’s reverse direction and the object’s surface normal exceeds 40 degrees. Such conditions can result in unreliable feature detection. Consequently, this hinders the perfor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/7/1111 |
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| author | Yukai Wang Yinghui Wang Wenzhuo Li Yanxing Liang Liangyi Huang Xiaojuan Ning |
| author_facet | Yukai Wang Yinghui Wang Wenzhuo Li Yanxing Liang Liangyi Huang Xiaojuan Ning |
| author_sort | Yukai Wang |
| collection | DOAJ |
| description | Maintaining stable image feature extraction under viewpoint changes is challenging, particularly when the angle between the camera’s reverse direction and the object’s surface normal exceeds 40 degrees. Such conditions can result in unreliable feature detection. Consequently, this hinders the performance of vision-based systems. To address this, we propose a feature point extraction method named Large Viewpoint Feature Extraction (LV-FeatEx). Firstly, the method uses a dual-threshold approach based on image grayscale histograms and Kapur’s maximum entropy to constrain the AGAST (Adaptive and Generic Accelerated Segment Test) feature detector. Combined with the FREAK (Fast Retina Keypoint) descriptor, the method enables more effective estimation of camera motion parameters. Next, we design a longitude sampling strategy to create a sparser affine simulation model. Meanwhile, images undergo perspective transformation based on the camera motion parameters. This improves operational efficiency and aligns perspective distortions between two images, enhancing feature point extraction accuracy under large viewpoints. Finally, we verify the stability of the extracted feature points through feature point matching. Comprehensive experimental results show that, under large viewpoint changes, our method outperforms popular classical and deep learning feature extraction methods. The correct rate of feature point matching improves by an average of 40.1 percent, and speed increases by an average of 6.67 times simultaneously. |
| format | Article |
| id | doaj-art-5ad25e1faef3471585995d92b15fab3a |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-5ad25e1faef3471585995d92b15fab3a2025-08-20T03:08:55ZengMDPI AGMathematics2227-73902025-03-01137111110.3390/math13071111LV-FeatEx: Large Viewpoint-Image Feature ExtractionYukai Wang0Yinghui Wang1Wenzhuo Li2Yanxing Liang3Liangyi Huang4Xiaojuan Ning5School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, ChinaSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USADepartment of Computer Science & Engineering, Xi’an University of Technology, Xi’an 710048, ChinaMaintaining stable image feature extraction under viewpoint changes is challenging, particularly when the angle between the camera’s reverse direction and the object’s surface normal exceeds 40 degrees. Such conditions can result in unreliable feature detection. Consequently, this hinders the performance of vision-based systems. To address this, we propose a feature point extraction method named Large Viewpoint Feature Extraction (LV-FeatEx). Firstly, the method uses a dual-threshold approach based on image grayscale histograms and Kapur’s maximum entropy to constrain the AGAST (Adaptive and Generic Accelerated Segment Test) feature detector. Combined with the FREAK (Fast Retina Keypoint) descriptor, the method enables more effective estimation of camera motion parameters. Next, we design a longitude sampling strategy to create a sparser affine simulation model. Meanwhile, images undergo perspective transformation based on the camera motion parameters. This improves operational efficiency and aligns perspective distortions between two images, enhancing feature point extraction accuracy under large viewpoints. Finally, we verify the stability of the extracted feature points through feature point matching. Comprehensive experimental results show that, under large viewpoint changes, our method outperforms popular classical and deep learning feature extraction methods. The correct rate of feature point matching improves by an average of 40.1 percent, and speed increases by an average of 6.67 times simultaneously.https://www.mdpi.com/2227-7390/13/7/1111feature pointslarge viewpointASIFTAGASTFREAK |
| spellingShingle | Yukai Wang Yinghui Wang Wenzhuo Li Yanxing Liang Liangyi Huang Xiaojuan Ning LV-FeatEx: Large Viewpoint-Image Feature Extraction Mathematics feature points large viewpoint ASIFT AGAST FREAK |
| title | LV-FeatEx: Large Viewpoint-Image Feature Extraction |
| title_full | LV-FeatEx: Large Viewpoint-Image Feature Extraction |
| title_fullStr | LV-FeatEx: Large Viewpoint-Image Feature Extraction |
| title_full_unstemmed | LV-FeatEx: Large Viewpoint-Image Feature Extraction |
| title_short | LV-FeatEx: Large Viewpoint-Image Feature Extraction |
| title_sort | lv featex large viewpoint image feature extraction |
| topic | feature points large viewpoint ASIFT AGAST FREAK |
| url | https://www.mdpi.com/2227-7390/13/7/1111 |
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