This paper proposes a matching method for medical image registration, which combined with SURF (Speeded up Robust Features) algorithm and the improved R-RANSAC (the Randomized of Random Sample Consensus) algorithm. Firstly, this algorithm extracts featured points with SURF algorithm from images and matches similar featured points with Euclidean distance. Secondly, the RRANSAC algorithm is used ...

Abstract—In this paper, we propose a modified version of the Random Sample Consensus (RANSAC) method for Interferometric Synthetic Aperture Radar (InSAR) image registration based on the ScaleInvariant Feature Transform (SIFT). Because of speckle, the “maximization of inliers” criterion in the original RANSAC cannot obtain the optimal results. Since in InSAR image registration, the registration ...

In this paper, we proposed a new feature based image mosaic algorithm. The improved RANSAC homography algorithm based on the modified media flow filter, to detect wrong matches for improving the stability of the normal RANSAC homography algorithm. The method improved the local registration between neighboring images. Experiments and Statistical Analysis show that our mosaic method is robust. Ke...

Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false...

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy speed when registering large point clouds. To meet this challenge, improved iterative closest (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), 3D context (3DSC) ...

Crucial information barely visible to the human eye is often embedded in a series of low-resolution images taken of the same scene. Super-resolution enables the extraction of this information by reconstructing a single image, at a higherresolution than is present in any of the individual images. This is particularly useful in forensic imaging, where the extraction of minute details in an image ...