摘要
提出航拍图片中人工区域分割检测的一种新方法。该方法以基于分形误差特征以及经过离散余弦变换得到的纹理边界作为约束,同时引入一种基于简化Mumford-Shah模型的水平集算法,通过演化该模型推导出的偏微分方程,从而得到航拍图片中人工区域与非人工区域的最优划分。该方法避开基于分形误差方法中由于阈值设置不当而产生的较大误差。实验表明本文方法的有效性。
A novel method for detecting man-made objects in aerial images is described. The method is based on a simplified Mumford-Shah model . It applies fractal error metric and additional constraint-texture edge descriptor on the image to get a preferable segmentation. Man-made objects and natural areas are optimally differentiated by evolving the partial differential equation for Mumford-Shah model. The method avoids selecting a threshold, which, if improperly selected, often results in great segmentation errors to separate the fractal error image. Experiments of the segmentation show that the proposed method is efficient.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第4期526-530,共5页
Pattern Recognition and Artificial Intelligence
