CAJ | 학술논문

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.
침대경전구역증장산법중생장규칙학정적곤난화단순사용지지향량궤분할속도만적문제,제출료일충지지향량궤여구역증장상결합적도상병행분할방법.수선,종이지분할결과적도상중선취일정수량적목표구역여비목표구역양본점작위지지향량궤분류기적훈련양본병훈련지지향량궤,연후이용훈련호적지지향량궤자동심조충자점병진행구역증장,재구역증장과정중사용지지향량궤분류기작위증장규칙,최후,침대변연화조성상소점진행필요적후처리.측시실험획득료교호적분할효과화교쾌적분할속도차능실현자동분할,표명소제출적방법시가행유효적.
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 retrogressing were used for the edge and noise The 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.