MultS-ORB: Multistage Oriented FAST and Rotated BRIEF

Shaojie Zhang, Yinghui Wang (), Jiaxing Ma (), Jinlong Yang, Liangyi Huang and Xiaojuan Ning
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Shaojie Zhang: 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
Jiaxing Ma: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Jinlong Yang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Liangyi Huang: School of Computing and Augmented Intelligence, Arizona State University, 1151 S Forest Ave, 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 13, 1-26

Abstract: Feature matching is crucial in image recognition. However, blurring caused by illumination changes often leads to deviations in local appearance-based similarity, resulting in ambiguous or false matches—an enduring challenge in computer vision. To address this issue, this paper proposes a method named MultS-ORB (Multistage Oriented FAST and Rotated BRIEF). The proposed method preserves all the advantages of the traditional ORB algorithm while significantly improving feature matching accuracy under illumination-induced blurring. Specifically, it first generates initial feature matching pairs using KNN (K-Nearest Neighbors) based on descriptor similarity in the Hamming space. Then, by introducing a local motion smoothness constraint, GMS (Grid-Based Motion Statistics) is applied to filter and optimize the matches, effectively reducing the interference caused by blurring. Afterward, the PROSAC (Progressive Sampling Consensus) algorithm is employed to further eliminate false correspondences resulting from illumination changes. This multistage strategy yields more accurate and reliable feature matches. Experimental results demonstrate that for blurred images affected by illumination changes, the proposed method improves matching accuracy by an average of 75%, reduces average error by 33.06%, and decreases RMSE (Root Mean Square Error) by 35.86% compared to the traditional ORB algorithm.

Keywords: blur-resilient matching; GMS; illumination blur; multistage feature matching; ORB feature points; PROSAC (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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