DALSM: A Direction-Aware Line Segment Matching Method

Abstract

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Matching line segments between a pair of images depicting the same scene is popularly achieved through the utilization of image feature point correspondences. However, existing methods of this type often exhibit inferior performance as they typically neglect other important image attributes. To address this fundamental problem, a novel direction-aware line segment matching (DALSM) method is proposed in this paper. Specifically, when establishing a potential match between a pair of line segments, two direction-aware image attributes are incorporated: the intersection angle between the two line segments and the gradient direction of each line segment. By integrating these direction-aware image attributes with feature point correspondences, the accuracy of the matching results is significantly improved. To further enhance matching performance, a prediction-correction scheme is also developed and exploited. In the prediction stage, a set of loose geometric constraints is used to filter out low-confidence line match candidates. Subsequently, in the correction stage, the aforementioned direction-aware image attributes are employed to assess similarities among line match candidates. Experimental results on benchmark datasets demonstrate that the proposed DALSM outperforms current state-of-the-art methods clearly.