摘要
针对传统活动轮廓对图像分割鲁棒性较差的问题,将基于区域的轮廓模型和基于梯度的轮廓模型通过图像熵与图像梯度和进行结合。通过图像熵与图像梯度和建立基于梯度与基于区域结合的活动轮廓模型。将水平集函数嵌入到模型中,对模型结果进行连续分割,并进行拓扑变化。采用窄带方法进行快速演化。实验证明,该方法有较好的鲁棒性和较快的分割速度,对图像分割理论的发展提供了新的研究途径。
To solve the problem of weak robustness in image segmentation of general active contour, an active contour model combined region-based model with gradient-based model is proposed using the entropy energy and gradient sum. Firstly, active contour model is constructed via fitting the gradient information and region information by use of entropy energy and gradient of image. Secondly, the level set is embed in the mentioned model to follow the curve change of topological structure. Finally, the evolution. The experiment results show that the proposed The study of the model is able to provide a new research narrow band method is used to increase the speed of model has a better robustness with faster evolution speed. way to develop the theory of image segmentation.
出处
《吉林大学学报（信息科学版）》
CAS
2016年第2期278-282,共5页
Journal of Jilin University（Information Science Edition）
基金
国家留学基金资助项目(201308220163)
国家自然科学基金资助项目(61303132)
教育部国际合作科研基金资助项目(Z2011138)
关键词
图像分割
熵
水平集
活动轮廓
image segmentation
entropy energy
level set
active contour model
