Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal
Ming Zhao, Yongpeng Wu, Shengda Pan, Fan Zhou, Bowen An, Andr\'e Kaup

TL;DR
This paper introduces an automatic image registration method that effectively handles images with affine deformations and inconsistent content by combining line-support region segmentation with geometrical outlier removal, improving accuracy across diverse image types.
Contribution
The proposed approach uniquely integrates line-support region segmentation with a novel geometrical outlier removal technique for robust registration of challenging images.
Findings
Outperforms existing methods on diverse image datasets
Effective in registering images with affine deformations and inconsistent content
Demonstrates robustness across remote sensing and map images
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…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Image and Object Detection Techniques · Advanced Image and Video Retrieval Techniques
