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改进的几何活动轮廓演化及其在目标跟踪中的应用 被引量：2

Improved Evolution of Geodesic Active Contour and its Application to Target Tracking
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摘要 为提高几何活动轮廓分割算法的分割效率和准确性,设计了新的边缘检测与跟踪算法.首先采用矢量图像计算图像的梯度值,并设计能够自适应调整阈值的边缘指示函数,进而提出改进的变分水平集演化模型;然后设计基于该改进模型的边缘检测算法,并在无迹卡尔曼滤波器框架下设计了运动目标的跟踪算法.实验结果表明,文中算法不但显著地提高了轮廓演化模型的灵活性和收敛速度,而且对阴影、遮挡、目标形变和背景干扰等具有较好的鲁棒性. In order to improve the efficiency and accuracy of geometric active contour model-based segmentation algorithm, a novel edge detection and tracking algorithm is presented. Firstly, the gradient of an image is calculated according to vector image, and an edge indicator with adaptive threshold is proposed. Secondly, an improved evolution model using variational level set is put forward. Then, on the basis of this model, an improved edge detection algorithm is proposed, and a target tracking algorithm is designed in the framework of unscented Kalman filter. Experimental results demonstrate that the proposed algorithm not only increases the convergence rate and flexibility of active contour evolution model significantly but also possesses strong robustness to such interferences as shadow, occlusion, object deformation and background interference.
作者 宋佳声 胡国清 焦亮
机构地区 华南理工大学机械与汽车工程学院 集美大学轮机工程学院
出处 《华南理工大学学报（自然科学版）》 EI CAS CSCD 北大核心 2015年第1期72-78,共7页 Journal of South China University of Technology(Natural Science Edition)
基金 福建省教育厅科研项目(JA14175)~~
关键词 几何活动轮廓 水平集方法 目标跟踪 无迹卡尔曼滤波器 geodesic active contour level set method target tracking unscented Kalman filter
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
作者简介 宋佳声（1976-），男，博士，集美大学讲师，主要从事图像处理与智能系统研究．E—mail：soongjs@gmail．com
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参考文献15

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共引文献3

同被引文献24

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华南理工大学学报（自然科学版）
2015年 第1期

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