摘要
针对活动轮廓模型利用水平集函数演化来分割图像时,只能分割灰度均匀的图像问题以及容易陷入能量泛函局部极小值的缺点,提出一种新的图像分割模型。模型将区域中的局部和全局信息融合的活动轮廓模型与边界模型相结合,然后利用图切割进行优化。实验表明,该方法对初始曲线不敏感,能分割灰度不均的自然图像,避免陷入局部极小,并能有效提高图像分割的速度和精度。
As the active contour model segments images using level set formulation, such formulation results in very slow algorithms that get easily stuck in local solutions and only segment image with intensity homogeneity. In this paper, a new model combining region-based with geodesic active contours is proposed for image segmentation. The new energy functional can be iteratively minimized by graph cut algorithms with high computational efficiency compared with the level set framework. Experiment results show that the proposed model can effectively and efficiently segment images with intensity inhomogeneity. The method is less sensitive to the location of initial contour and can also avoid local minima solutions.
出处
《图学学报》
CSCD
北大核心
2015年第5期756-762,共7页
Journal of Graphics
基金
国家自然科学基金资助项目(51105310)
关键词
图像分割
活动轮廓
水平集方法
图割
image segmentation
active contours
level set method
graph cut
