格点上的一种统计分割模型(英文)

Statistical segmentation model on lattices

为了克服Kim-Fisher模型实现难度大、运行速度慢的问题,提出了离散的近似Kim-Fisher模型.该离散模型的目标函数直接定义在格点上,采用贪心法进行优化.首先,把图像的灰度值视为离散的随机变量,从而可以采用更为简单的方法估计条件熵.其次,针对基于水平集技术的二区域和多区域图像分割,提出一种无须扩展的统一的方法.最后,还提出一种多标号格点上曲线长度的近似方法,该方法比现有的方法更加准确.实验结果表明,同传统的连续Kim-Fisher模型相比,所提出的模型在取得相当的分割效果的同时,简化了实现过程,并大大降低了运行时间.

To reduce the difficulty of implementation and shorten the runtime of the traditional Kim-Fisher model, an entirely discrete Kim-Fisher-like model on lattices is proposed. The discrete model is directly built on the lattices, and the greedy algorithm is used in the implementation to continually decrease the energy function. First, regarding the gray values in images as discrete-valued random variables makes it possible to make a much simpler estimation of conditional entropy. Secondly, a uniform method within the level set framework for two-phase and multiphase segmentations without extension is presented. Finally, a more accurate approximation to the curve length on lattices with multi-labels is proposed. The experimental results show that, compared with the continuous Kim-Fisher model, the proposed model can obtain comparative results, while the implementation is much simpler and the runtime is dramatically reduced.