复杂背景下基于AD-GAC模型和最大熵阈值法的叶片病斑分割
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摘要
旨在研究复杂背景下叶片病斑的分割。由于复杂背景会带来巨大的噪声,产生过多的边缘和灰度值不均匀的区域,很容易导致过分割的现象,因此在复杂背景下,很难通过1次分割就完成对叶片病斑的分割。为了解决复杂背景下过分割的现象,提出两步分割的策略。第1步先用笔者提出的各向异性扩散测地线活动轮廓模型(anisotropic diffusion geodesic active contour model,简称AD-GAC模型)进行预分割,在此过程中构造新的边缘检测函数(edge stop function,简称ESF);第2步通过最大熵阈值法完成最终的分割。随后,提取并计算预分割部分各像素灰度值的最大熵,以得到病斑部分与叶片部分的灰度值阈值,通过阈值来完成最后1步的分割。通过MATLAB仿真,可以证明该算法可以有效地将病斑从复杂背景下的叶片上分割出来。研究结果后续的病斑识别作了铺垫。
引文
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