A novel approach for line detection and matching is proposed, aimed at achieving good performance
with low-textured scenes, under uncontrolled illumination conditions. Line detection is performed by
means of phase-based edge detector over Gaussian scale-space, followed by a multi-scale fusion stage
which has been proven to be profitable in minimizing the number of fragmented and overlapped
segments. Line matching is performed by an iterative process that uses structural information collected
through the use of different line neighborhoods, making the set of matched lines grow robustly at each
iteration. Results show that this approach is suitable to deal with low-textured scenes, and also robust
under a wide variety of image transformations.
Keywords: Line detection and matching, Man-made environments, Differencial phase congruency, Adaptive incremental, Context-based matching