基于信息熵的自适应高斯金字塔的 LSD 算法改进

针对 LSD(Line Segment Detector)算法在对图像中连续的边缘进行提取时, 结果中常常呈现线段不连续的问题, 提出一种基于信息熵的自适应高斯金字塔的 LSD 改进算法。 该算法首先通过计算处理后的图像与原图之间的互信息熵, 确定高斯金字塔的层数与层内图像数量, 构建出自适应高斯金字塔; 其次使用改进的大津阈值, 根据图像的梯度峰值将图像分割成不同区域并计算相应梯度阈值, 分离出图像背景; 最后根据梯度角度寻找线段, 并通过赫尔姆霍兹准则验证线段。 仿真结果表明, 该算法解决了 LSD 算法提取线段不连续的问题;与其他流行算法 Hough 变换(Hough Transform)、 PPHT(Progressive Probabilistic Hough Transform)、 LSWMS(Line Segment detection using Weighted Mean Shift)、 LSD、 EDLines 比较, 能提取出更多有意义的线段。

Improved LSD Algorithm Based on Entropy Adaptive Gaussian Pyramid

Because the LSD (Line Segment Detector) algorithm, when extracting continuous edges in an image, often has the problem of line segment discontinuity in results, we proposed an improved LSD algorithm based on information entropy. Firstly, the algorithm calculates the mutual information entropy between the processed image and the original image to determine the number of layers of gauss pyramid and the number of images in the layers, to build an adaptive Gaussian pyramid. Secondly, the improved otsu threshold is used to divide the image into different regions according to the gradient peak value of the image,and the corresponding gradient threshold is calculated to separate the image background. Finally, line segments are found according to the gradient angle and verified by helmholtz criterion. The simulation results show that the algorithm solves the problem of LSD algorithm in extracting the discontinuous line segments. Compared with other popular algorithms Hough Transform, PPHT(Progressive Probabilistic Hough Transform), LSWMS(Line Segment detection using Weighted Mean Shift), LSD, EDLines, more meaningful Line segments were extracted.