Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1037-1041, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1037-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1037-1041, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1037-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

LINE SEGMENTS MATCHING ALGORITHMl C0MBINING MSLD DESCRIPTION AND CORRESPONDING P0INTS CONSTRAINT

X. Zhang and J. Zhang X. Zhang and J. Zhang
  • School of Geomatics, Liaoning Technical University,123000, Fuxin, Liaoning, China

Keywords: Mean-Standard Deviation Descriptor, Corresponding points, Pixel support region, Euclidean distance, Nearest Neighbor Distance Ratio

Abstract. Aiming at stability of line descriptor in the process of line segment matching, MSLD mean-standard deviation descriptor with corresponding points constraint method is proposed.The method is based on close-range images corresponding points matching and line extraction, in the beginning, determined the points which have the closest distance between the both sides of the target lines on the reference images, virtual line is composed by connecting corresponding points on the search image, and the line which intersects virtual line on the search image is defined as the candidate line segment. Then calculating the MSLD description of the straight lines and the candidate lines respectively, the specific construction steps are as followed: (1) gradient direction and normal direction of the straight line should be determined firstly; (2) for each pixel on the straight line, a rectangular area which is defined as Pixel Support Region (PSR) is established along the gradient direction and the normal direction, and the PSR is decomposed into several same size sub-regions in the normal direction; (3) recording each sub-region the gradient vectors of four directions to obtain a four-dimensional feature vector, and the gradient description matrix of straight line L is composed of all sub-regions feature vectors; (4) mean and standard deviation of the description matrix should be calculated by the row vector, then mean and standard deviation vectors should be normalized to obtain the normalized mean-standard deviation description. Finally, the similarity between the target lines and each candidate lines descriptor is calculated based on the Euclidean distance, using the nearest neighbor distance ratio to determine the corresponding line. Typical region image is selected to perform line matching experiment in this paper, results show that the proposed method has great stability and matching accuracy.