Given that the fracture situation affects the results and reliability of straight line matching

a new line matching method for close-range images under multi-conditions is proposed. First

the initial corresponding points are obtained using the scale-invariant feature transform algorithm and are optimized by random sample consensus. The affine transform matrix is computed based on the final corresponding points. The dense matching accuracy is improved through affine transform

Harris interest value

and least square method based on the constructed points. Second

the lines are extracted using the Freeman chain code priority algorithm

and the initial matching results are obtained based on the position relationship between the dense matching points and the lines in the searching area. Third

the initial corresponding lines are optimized by the line coincidence degree

and the endpoints of the extracted lines are determined under epipolar constraint. s A set of close-range images that contain rotation

scale change

and occlusion are used in line extraction experiments. The experiment results show that

compared with other line matching methods

the proposed method successfully matches 1.07 to 4.1 times more lines and improves the accuracy of straight line matching by 0.6% to 53.3%. The proposed method also outperforms the existing methods in terms of accuracy and robustness. By setting multi-conditions

the searching area of line features is effectively decreased in stereo image matching

thereby significantly improving the matching efficiency. Experiments are performed on close-range images under different geometric transformations. The proposed method can be used to solve line break and occlusion problems.