梯度熵改进边缘检测的自适应阈值曲面分割算法

An Adaptive Threshold Surface Segmentation Algorithm Based on Improved Edge Detection Using Gradient Entropy

光照不均匀往往造成背景亮度不均和灰度分布范围较大,会导致图像分割困难和不准确.考虑图像的边缘信息受光线变化相对不敏感,引入梯度熵信息对Canny算法进行改进提取准确合适的边缘.采用最小二乘法的多项式曲面拟合获得阈值曲面,进而提出了基于梯度熵改进边缘检测的自适应阈值曲面分割算法.对多种背景灰度分布不均匀的图像进行算法验证,实验结果表明,算法可以获得准确的边缘信息,对多种灰度分布不均匀图像均取得较好分割结果.

To solve the segmentation problem of the non-uniform illumination and the wide range distribution of grayscale gradation in target and background,considering the information on edge of an image is insensitive to the change of light,quoting of gradient entropy to improve Canny algorithm to detect appropriate edge.Using polynomial curved surface fitting of least square method to get threshold surface to segment targets in the uneven background,so an adaptive threshold surface segmentation algorithm with improved edge detection has been proposed.Various non-uniform distribution of grayscale gradation in background images were verified,the experiment results indicated that the algorithm could get correct edge information,and get relatively good segmentation results to the images with non-uniform distribution of grayscale gradation.