摘要
针对目标和背景的面积相差很大时,最大类间方差阈值法(Otsu阈值法)得到的阈值是"有偏"的,从而造成阈值图像分割失败的问题,提出一种最大散度差准则的阈值图像分割方法。最大散度差准则以广义散度差——类间方差减去C倍的类内方差作为分离性度量,同时考虑类间方差和类内方差在可分性中的作用,可有效克服最大类间方差阈值法(Otsu阈值法)的阈值"偏移"现象。实验结果表明:通过选择适当的参数C,该方法能得到比最大类间方差法更好的分割结果。
Previous research results show that threshold obtained by maximum between-class variance method （i.e. Otsu method） is biased when the area of object and background differs significantly and may lead to failure segmentation. A new image segmentation method based on maximum scatter difference is proposed. Maximum scatter difference uses generalized scatter difference,i.e.,the difference of between-class scatter difference and C times of within-class scatter difference,as the discriminant measure. Maximum scatter difference considers simultaneously the function of discrimination of between-class scatter difference within-class scatter difference. The proposed method can prevents the threshold biasing from maximum between-class variance method. Experimental results show that the proposed method can obtain better segmentation result than otsu method by appropriately selecting parameter C.
出处
《应用光学》
CAS
CSCD
北大核心
2010年第3期403-407,共5页
Journal of Applied Optics
基金
国家自然科学基金(60877047)
河北省自然科学基金(F2008000873
关键词
图像分割
最大散度差
最大类间方差
image segmentation
maximum scatter difference
maximum between-class variance
