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Article

SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage

1
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
2
Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, USA
3
Cubiscan, Inc., Farmington, UT 84025, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 810; https://doi.org/10.3390/electronics9050810
Received: 28 March 2020 / Revised: 10 May 2020 / Accepted: 12 May 2020 / Published: 15 May 2020
(This article belongs to the Special Issue Convolutional Neural Networks and Vision Applications)
Feature description has an important role in image matching and is widely used for a variety of computer vision applications. As an efficient synthetic basis feature descriptor, SYnthetic BAsis (SYBA) requires low computational complexity and provides accurate matching results. However, the number of matched feature points generated by SYBA suffers from large image scaling and rotation variations. In this paper, we improve SYBA’s scale and rotation invariance by adding an efficient pre-processing operation. The proposed algorithm, SR-SYBA, represents the scale of the feature region with the location of maximum gradient response along the radial direction in Log-polar coordinate system. Based on this scale representation, it normalizes all feature regions to the same reference scale to provide scale invariance. The orientation of the feature region is represented as the orientation of the vector from the center of the feature region to its intensity centroid. Based on this orientation representation, all feature regions are rotated to the same reference orientation to provide rotation invariance. The original SYBA descriptor is then applied to the scale and orientation normalized feature regions for description and matching. Experiment results show that SR-SYBA greatly improves SYBA for image matching applications with scaling and rotation variations. SR-SYBA obtains comparable or better performance in terms of matching rate compared to the mainstream algorithms while still maintains its advantages of using much less storage and simpler computations. SR-SYBA is applied to a vision-based measurement application to demonstrate its performance for image matching. View Full-Text
Keywords: feature description algorithm; log-polar coordinates; scale-invariance; rotation-invariance; vision-based measurement feature description algorithm; log-polar coordinates; scale-invariance; rotation-invariance; vision-based measurement
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MDPI and ACS Style

Yu, M.; Zhang, D.; Lee, D.-J.; Desai, A. SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage. Electronics 2020, 9, 810. https://doi.org/10.3390/electronics9050810

AMA Style

Yu M, Zhang D, Lee D-J, Desai A. SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage. Electronics. 2020; 9(5):810. https://doi.org/10.3390/electronics9050810

Chicago/Turabian Style

Yu, Meng, Dong Zhang, Dah-Jye Lee, and Alok Desai. 2020. "SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage" Electronics 9, no. 5: 810. https://doi.org/10.3390/electronics9050810

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