Feature Line Matching Based on the Principal Component Similarity Constraint of Line Segment Primitive Support Region

To solve the problem that the accurate location of line segment endpoint is difficult to be identified in line feature matching, a method of combining the main points of the edge and feature line matching based on the principal component similarity constraint of line segment primitive support region is proposed in this paper. Firstly, ASIFT algorithm is used to match the corresponding points and calculate the affine transformation matrix. Secondly, Freeman chain code is applied to obtain the main points of the edge after an edge is detected through the Canny operator, and the main points of the edge are taken as the matching primitive to be matched independently combined with affine transformation, epipolar constraint and Harris interest value constraint. Finally, the initial matching results are checked through the neighborhood principal component similarity consistency of the line segment. The different types of close range images are taken as the experimental data, and the results verify the validity and universality of the algorithm. The method strives to achieve the line matching by points substituting lines, reduce the complexity of the line matching algorithm, use the principal component similarity constraint of the segment line to check the results, and make full use of color information of close images and improve the reliability of the matching results.