文摘
This paper presents a method that performs the rectification of planar objects. Based on the 2D Manhattan world assumption (i.e., the majority of line segments are aligned with principal axes), we develop a cost function whose minimization yields a rectification transform. We parameterize the homography with camera parameters and design a cost function which encodes the measure of line segment alignment. Since there are outliers in the line segment detection, we also develop an iterative optimization scheme for the robust estimation. Experimental results on a range of images with planar objects show that our method performs rectification robustly and accurately.