适应复杂背景的C-V模型  

C-V Model Adaptive to Complex Background

作　　者：崔华[1] 高立群[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2009年第6期765-768,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(60475036);国家高技术研究发展计划项目(2004AA412030)

摘　　要：当图像中只有目标和背景两类分片光滑区域时,C-V模型可以取得很好的分割效果,然而需要从复杂背景中提取目标边缘时,该模型往往无法得到正确的结果.针对这一问题,在C-V模型和均值平移技术的基础上提出了适应复杂背景的C-V模型.该模型能够根据图像特征空间分段地确定轮廓曲线的内部区域和外部区域.实验结果表明,所提出的适应复杂背景的C-V模型对复杂背景有更强的适应能力,能够从复杂背景中成功地提取到连续光滑的目标边缘.The segmentation by C-V models can achieve good effect only if there are two smoothly partitioned zones in an image, i.e. the object zone and background zone. However, when extracting image boundary from complex background, C-V model often cannot get correct results. To solve the problem, the conventional C-V model and mean shift are used to develop a C-V model to adapt to the complex background. This model can determine piecewise the interior and exterior zones of contour curves. Experimental results showed that the model developed is more adaptive to complex background in comparison with the conventional C-V model and can extract smoothly uninterrupted image boundaries from complex background.

关 键 词：边缘提取 C-V模型 均值平移 复杂背景 光滑边缘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]