摘要
针对经典区域增长算法中生长规则确定的困难和单纯使用支持向量机分割速度慢的问题,提出了一种支持向量机与区域增长相结合的图像并行分割方法。首先,从已知分割结果的图像中选取一定数量的目标区域与非目标区域样本点作为支持向量机分类器的训练样本并训练支持向量机,然后利用训练好的支持向量机自动寻找种子点并进行区域增长,在区域增长过程中使用支持向量机分类器作为增长规则,最后,针对边缘和噪声像素点进行必要的后处理。测试实验获得了较好的分割效果和较快的分割速度且能实现自动分割,表明所提出的方法是可行有效的。
In order to solve the difficulty of determining the growth rules in conventional regional growth algorithm and the slowly of support vector machine segmentation algorithm, an image segmentation method combined support vector machine and regional growth was proposed. Firstly, selected a certain numbers of sample point from target area and nontarget area and trained the support vector machine classification,then used the trained classification search seed point and regional growing, the support vector machine classification was used as growth rules, the last, some necessary retrogrossing were used for the edge and noise. IThe experimental results show that this algorithm is feasible and it performs better than conventional region growth segmentation algorithm and faster then conventional support vector machine segmentation algorithm.
出处
《计算机科学》
CSCD
北大核心
2010年第5期237-239,共3页
Computer Science
基金
国家国际科技合作重大专项(No.2007DFB30320)
黑龙江省教育厅科技计划项目(No.11531048)
哈尔滨市科技创新人才研究专项资金项目(No.2008RFQXS062)资助
