LV-FeatEx: Large Viewpoint-Image Feature Extraction

Yukai Wang, Yinghui Wang (), Wenzhuo Li (), Yanxing Liang, Liangyi Huang and Xiaojuan Ning
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Yukai Wang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Yinghui Wang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Wenzhuo Li: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Yanxing Liang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Liangyi Huang: School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
Xiaojuan Ning: Department of Computer Science & Engineering, Xi’an University of Technology, Xi’an 710048, China

Mathematics, 2025, vol. 13, issue 7, 1-24

Abstract: 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.

Keywords: feature points; large viewpoint; ASIFT; AGAST; FREAK (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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