Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal

• URL: http://arxiv.org/abs/2204.00832v1
• Date: Sat, 2 Apr 2022 10:47:16 GMT
• Title: Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal
• Authors: Ming Zhao, Yongpeng Wu, Shengda Pan, Fan Zhou, Bowen An, Andr\'e Kaup
• Abstract summary: This paper proposes an automatic image registration approach through line-support region segmentation and geometrical outlier removal (ALRS-GOR) It is designed to address the problems associated with the registration of images with affine deformations and inconsistent content. Various image sets have been considered for the evaluation of the proposed approach, including aerial images with simulated affine deformations.
• Score: 17.90609572352273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The implementation of automatic image registration is still difficult in various applications. In this paper, an automatic image registration approach through line-support region segmentation and geometrical outlier removal (ALRS-GOR) is proposed. This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations. To begin with, line-support regions, namely a straight region whose points share roughly the same image gradient angle, are extracted to address the issues of inconsistent content existing in images. To alleviate the incompleteness of line segments, an iterative strategy with multi-resolution is employed to preserve global structures that are masked at full resolution by image details or noise. Then, Geometrical Outlier Removal (GOR) is developed to provide reliable feature point matching, which is based on affineinvariant geometrical classifications for corresponding matches initialized by SIFT. The candidate outliers are selected by comparing the disparity of accumulated classifications among all matches, instead of conventional methods which only rely on local geometrical relations. Various image sets have been considered in this paper for the evaluation of the proposed approach, including aerial images with simulated affine deformations, remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal), and map images with inconsistent annotations. Experimental results demonstrate the superior performance of the proposed method over the existing approaches for the whole data set.

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