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参数自适应的KPCA先验形状约束目标分割 被引量：3

Shape prior constrained KPCA object segmentation with parameter adaption
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摘要 为克服固定先验形状在分割可变形目标时的困难,提出一种基于核主元分析(KPCA)的参数自适应先验形状约束水平集分割方法。首先使用KPCA变换获取目标先验形状特征空间的基底向量;其次用Parzen窗估计待分割图像的灰度分布以构造图像数据能量项;然后使用仿射变换对齐图像感兴趣区域与先验形状,从而将目标形状先验知识集成到分割模型中;最后在基于水平集方法求解演化方程时自适应地估计参数,实现形变目标的分割。实验结果表明,相比于CV(Chan-Vese)模型和单先验形状约束的水平集方法,该模型能够有效地分割不同姿态的目标形状。 In order to solve the problem of deformable objects segmentation with a fixed shape prior, a shape prior con- strained and parameter adaption level set segmentation method based on kernel principal component analysis （KPCA） is proposed. First, the KPCA method is used to get the base vectors in the shape prior feature space. Then, the Parzen win- dow method is used to estimate the results of the original image for image data term and an affine transformation is performed to align the image region of interest and prior shape training set to add shape priors to the segmentation model. At last, a parameter adaptive method is introduced when solving the evolution equation based on level set method. Experimental re- suits show that our method can effectively segment objects with different attitudes in comparison with the Chan-Vese （CV） model and single prior shape constrained level set methods.
作者 沈霁 李元祥 周则明
机构地区 上海交通大学航空航天学院 解放军理工大学气象学院
出处 《中国图象图形学报》 CSCD 北大核心 2013年第7期783-789,共7页 Journal of Image and Graphics
基金 国家自然科学基金项目(41174164 61174196) 国家高技术研究发展计划(863)基金项目
关键词 图像分割 先验形状 核主元分析 仿射变换 参数自适应 image segmentation shape prior kernel principal component analysis （KCPA） affine transformation parameter adaption
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献16

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二级参考文献30

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共引文献30

同被引文献39

  • 1周建民,陈超,涂文兵,刘依,胡艳斌.红外热波技术、有限元与SVM相结合的复合材料分层缺陷检测方法[J].仪器仪表学报,2020,41(3):29-38. 被引量：21
  • 2李林茹,高双喜,曹淑服.基于小波变换和梯度矢量流Snake模型的ERS-1 SAR图像的海岸线探测[J].河北工业科技,2004,21(4):24-26. 被引量：8
  • 3马小峰,赵冬至,邢小罡,张丰收,文世勇,杨帆.海岸线卫星遥感提取方法研究[J].海洋环境科学,2007,26(2):185-189. 被引量：110
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引证文献3

二级引证文献5

中国图象图形学报
2013年 第7期

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