Complex shearlets and rotary phase congruence tensor for corner detection. (August 2022)

• Record Type: Journal Article
• Authors: Wang, Mingzhe; Sun, Changming; Sowmya, Arcot
• Is Part Of: Pattern recognition. Volume 128(2022)
• Publication Date: 2022-08
• Subjects: Complex shearlets -- Phase congruence -- Rotary structure tensor -- Multi-scale analysis -- Corner detection; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108606
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Proposed complex shearlets can overcome the weakness of the current shearlets. Idea fully using both amplitude and phase information to detect corners is novel. A new phase congruence model with high localization ability and noise tolerance. A new rotation-normalized tensor using all amplitudes and phases to detect corners. The work yields improvements on the current state-of-the-art in corner detection. Abstract: Corner detection algorithms based on multi-scale analysis attract more attention due to their promising performance. However, they only consider amplitude information, neglect phase information and partially utilize multi-scale decomposition coefficients to detect corners. This limits their detection accuracy, repeatability and localization ability. This paper describes a new multi-scale analysis based corner detector. To overcome the problems of bilateral margin responses, edge extension and lack of phase information in traditional shearlets, a novel complex shearlet transform is proposed to better localize distributed discontinuities and especially to extract phase information from geometrical features. Moreover, a new rotary phase congruence tensor is proposed to utilize all amplitude and phase information for corner detection. Its tolerances to noise and ability for corner localization are improved further by screening and normalizing the amplitude information. Experimental results demonstrate that the localization ability and detection accuracy of the proposed method are superior to current detectors, and its repeatability is generally higher than current detectors and recent machine learning based interest point detectors.