Automatic Registration of Images With Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Remova - 2018


The implementation of automatic image registration is still troublesome in various applications. In this Project, an automatic image registration approach through line-support region segmentation and geometrical outlier removal is proposed. This new approach is designed to handle the problems associated with the registration of pictures with affine deformations and inconsistent content, such as remote sensing pictures with totally 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 handle the problems of inconsistent content existing in pictures. To alleviate the incompleteness of line segments, an iterative strategy with multi-resolution is utilized to preserve international structures that are masked at full resolution by image details or noise. Then, geometrical outlier removal is developed to produce reliable feature point matching, which is based on affine-invariant geometrical classifications for corresponding matches initialized by scale invariant feature transform. The candidate outliers are selected by comparing the disparity of accumulated classifications among all matches, instead of standard strategies which only rely on local geometrical relations. Numerous image sets are thought of during this Project for the evaluation of the proposed approach, as well as aerial pictures with simulated affine deformations, remote sensing optical and artificial aperture radar pictures taken at totally different things (multispectral, multisensor, and multitemporal), and map pictures with inconsistent annotations. Experimental results demonstrate the superior performance of the proposed methodology over the present approaches for the entire knowledge set.

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