A Novel Line Matching Algorithm Based On Neural Networks

• 1、Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：Line matching is one of the most important technologies in the field of computer vision. It is a key link in image stitching, 3D reconstruction and image registration and widely used in many applications. Compared with point matching, line contains more structure information of object and has strong anti-jamming ability to environmental noise, which attracts much attention of scholars. However, the inaccurate location of line endpoints brings great challenges to the research of line matching. In order to solve this problem, this paper proposed a novel line matching algorithm based on convolutional neural networks to extract line features. Firstly, the feature lines in the images are detected, and then the fixed size image blocks are cropped according to the lines. Secondly, the cropped image block is input to the Line Matching Network(LMNet), which is designed to extract line features, to judge whether the feature lines matched. Finally, the post-processing mechanism is used to eliminate the duplicate matching and the wrong matching. Experimental results show that the proposed algorithm can significantly improve the performance of line matching and effectively solve the problem of inaccurate location of line endpoints.

Keywords： line matching computer vision convolutional neural network