MSLD: A robust descriptor for line matching

Line matching plays an important role in many applications, such as image registration, 3D reconstruction, object recognition and video understanding. However, compared with other features (such as point, region matching), it has made little progress in recent years.In this paper, we investigate the problem of matching line segments automatically only from their neighborhood appearance, without resorting to any other constraints or priori knowledge. A novel line descriptor called mean–standard deviation line descriptor (MSLD) descriptor is proposed for this purpose, which is constructed by the following three steps: (1) For each pixel on the line segment, its pixel support region (PSR) is defined and then the PSR is divided into non-overlapped sub-regions. (2) Line gradient description matrix (GDM) is formed by characterizing each sub-region into a vector. (3) MSLD is built by computing the mean and standard deviation of GDM column vectors. Extensive experiments on real images show that MSLD descriptor is highly distinctive for line matching under rotation, illumination change, image blur, viewpoint change, noise, JPEG compression and partial occlusion.In addition, the concept of MSLD descriptor can also be extended to creating curve descriptor (mean–standard deviation curve descriptor, MSCD), and promising MSCD-based results for both curve and region matching are also demonstrated in this work.