Line detection algorithm based on adaptive gradient threshold and weighted mean shift

Abstract

Line detection is a classical problem in computer vision and image processing, and it is widely used as a basic method. Most of existing line detection algorithms are based on edge information, whose discontinuity limited the detection result. Meanwhile, some other algorithms only use gradient magnitudes, and neglect the function of gradient directions. In this paper, an adaptive gradient threshold and omni-direction line growing method based on line detection with weighted mean shift procedure and 2D slice sampling strategy (referred to as LSWMSAllDir) is proposed. It makes full use of the magnitudes and directions of the gradient to detect lines in the image. Experiments on synthetic data and real scene image data showed that the improve algorithm was the most accurate when compared with Progressive Probabilistic Hough Transform (PPHT), line segment detector (LSD), parameter free edge drawing (EDPF) and original line segment detection using weighted mean shift (LSWMS) algorithms.