Abstract
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.
Highlights
The process of image matching looks for corresponding targets in two images by analyzing the similarity and consistency of image contents, structures, features, relationships, textures and gray scales [1]
Scale-Invariant Feature Transform (SIFT), we propose an efficient method to represent the scale of the feature region by the location of its maximum gradient response along the radial direction in the Log-polar coordinate system
This paper proposes an algorithm called SR-SYnthetic BAsis (SYBA) to solve the scale and rotation invariance
Summary
The process of image matching looks for corresponding targets in two images by analyzing the similarity and consistency of image contents, structures, features, relationships, textures and gray scales [1]. Perform pyramid decomposition to images, and provide unique feature description at multiple scales using gradient magnitude and orientation computations. Invariant Scalable Keypoints (BRISK) [7] and Oriented FAST and Rotated BRIEF (ORB) [8] algorithms, as the improvements of BRIEF for scale and rotation invariance These binary feature descriptors use fewer bytes to describe the feature point and simplify the computations and reduce the size of storage. SIFT, we propose an efficient method to represent the scale of the feature region by the location of its maximum gradient response along the radial direction in the Log-polar coordinate system Using this scale representation, all feature regions in the image can be normalized to the same reference scale to provide scale invariance.
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