引入形状统计先验的轮廓提取的概率方法  

Introducing Statistical Shape Prior to Probabilistic Contour Extraction

作　　者：樊鑫[1] 梁德群[2] 

机构地区：[1]西安交通大学图像处理与识别研究所,西安710049 [2]大连海事大学信息工程学院,大连116026

出　　处：《计算机辅助设计与图形学学报》2005年第4期829-833,共5页Journal of Computer-Aided Design & Computer Graphics

摘　　要：提出了轮廓提取的概率方法,同时将物体形状的统计先验信息和轮廓线的平滑性结合到轮廓的概率估计中首先应用主元分析对低维空间概率采样生成高维形状统计分布的样本,根据形状样本得到轮廓控制点样本,而后结合描述平滑性的先验概率,运用序列蒙特卡罗方法实现轮廓估计对实际拍摄的人手图像的实验表明,针对特定的形状,该方法在杂乱背景。In this paper, statistical shape prior and smoothness of contours are simultaneously incorporated into probabilistic contour estimation. By means of principal component analysis, shape samples are generated from sampling a density in a lower dimensional space. Based on the state samples derived from the shape samples and on the prior density encoding smoothness, the contour with a specific shape is estimated by a sequential Monte-Carlo method. Extensive experiments on the real-world hand images show that the proposed approach significantly improves the robustness to cluttered background, occlusion and noise with respect to the objects with specific shapes.

关 键 词：轮廓提取 概率估计 序列蒙特卡罗方法 形状统计先验 主元分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]