Automatic Line Segment Registration Using Gaussian Mixture Model and Expectation-Maximization Algorithm

Abstract

Line segment registration (LSR) for image pairs is a challenging task but plays an important role in remote sensing and photogrammetry. This paper proposes a line segment registration method using Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) algorithm. Comparing to the conventional registration methods which consider the local appearance of points or line segments, the proposed method of LSR uses only the spatial relations between the line segments detected from an image pair, and it does not require the corresponding line segments sharing the same start points and end points. Although the proposed method is not confined to the transformation model between the image pair, the affine model, which is a simple and fast registration model and widely used in remote sensing, is taken to verify the proposed method. Various images including aerial images, satellite images and GIS data are used to test the algorithm, and test results show that the method is robust to different conditions, including rotation, noise and illumination. The results of the proposed method are compared with those of other line segment matching methods, and it is shown that the proposed method is superior in matching precision and performs better in less-texture or no-texture case.