摘要
针对传统十字分割模型对于二维直方图的划分不合理,提出一种曲线分割模型,并通过多条线段拟合简化该模型.在分割阈值的求解过程中,将二维阈值转换为一维阈值,且为获得快速算法,采用了迭代和优化搜索策略.实验表明,曲线分割模型比十字分割模型具有更优的抗噪性能,对于图像边缘形状保持得好,并且此算法的运算量大大降低.
A curve segrnetation model is presented, which overcomes the unreasonable division of two-dimensional histogram based on the traditional cross segmentation model. The simplified model is also get by multi-line fitting. In the process of solving the threshhold, the Mgorithn transfer the two-dimentional threshold to one-dimentional threshold. And in order to get the fast calculation, an iterative method and an optimizing search strategy is used. Compared with the cross segmetation model, the curve segmentation model has the better performance of anti-noise. It maintains the better shape of image edge, and the computation of algorithm is greatly reduced.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第9期20-23,28,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(60573028)
湖南省教育厅自然科学基金项目(07C526)
关键词
曲线分割模型
十字分割模型
拟合
阈值
迭代
优化搜索
curve segmentation model
cross segmentation model
fitting
threshold
iteration
optimizing search
