Two-view line matching algorithm based on context and appearance in low-textured images

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. HighlightsA novel approach for line detection and matching is proposed..Lines are detected through a fusion process over the Gaussian scale-space.Lines are matched based on their appearance, geometric properties and neighborhoods.Our proposal minimizes the number of fragmented and overlapped segments.Our proposal is suitable for low-textured scenes, under uncontrolled illumination.

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