Novel Similarity-Invariant Line Descriptor and Matching Algorithm for Global Motion Estimation

We present a new and fast line descriptor and matching algorithm to geometrically register video frames that are deformed by a similarity transformation and contain line segments but with very low texture details, such as navigation maps. Line segments are extracted from the frames, and each line is described with a novel line descriptor that does not depend on pixel intensities. The described lines are then matched together and the matches are input to an outlier removal algorithm in order to estimate the parameters of the transformation describing the global motion between the frames. We propose a method for fast parameter estimation of the transformation using line segments instead of points. Additionally, we apply this algorithm in the testing and error detection context of navigation systems, in which we show how to detect map jump artifacts that could occur during the development of these systems, leading to a jerky and unsmooth motion between the frames. The proposed descriptor and its matching algorithm are shown to be fast enough for online use and very robust against a wide range of translation, rotation, and scale changes. Furthermore, the error detection algorithm allows to detect almost all map jump artifacts while maintaining a very low number of false alarms.