An improved RANSAC algorithm for detection, extraction, and enhancement of noisy indoor house type image lines

Abstract

For noisy indoor house type images obtained by infrared scanning and other methods, they usually need to be further optimized before they can be used. This paper proposes an improved RANSAC algorithm based on the conventional RANSAC algorithm,and combines the least squares method to detect,extract and enhance lines of noisy indoor house type image.The binarization of the image is completed by threshold judgment division to avoid the excessive dispersion of pixels, which leads to the deviation of the subsequent RANSAC extraction model.Then, a recursive-based desalination method is proposed to desalinate the image, desalt the noise and improve the accuracy.When detecting a straight line, a sequential recording method is proposed to record and process the multi-segment line segments that are in the same straight line but not connected to ensure that there will be no omission or error. Finally, the extraction of multiple straight lines and line segments is realized by cyclically removing interior points, so as to complete the straight line detection and extraction enhancement process of the entire noisy indoor house type image. The actual results show that the method has high accuracy and timeliness, and can make the line detection and extraction completion rate reach about 90%, and can still maintain a very low error rate even in a heavily noisy environment.