基于超顺磁聚类的复杂红外图像分割算法

针对复杂红外图像分割问题,将非均匀Potts模型的热力学聚集运动看作是数据聚类,提出了基于超顺磁聚类的分割新算法.算法首先要指定控制系统的哈密尔顿函数,然后通过测量磁化率随温度变化的曲线来识别系统的不同相位,最后在超顺磁相位测量相邻自旋子的相关函数来将图像分割成子类.结合SW算法和Metropolis算法给出了一种新的产生马尔科夫过程的方法,该过程能够快速收敛于Boltzmann分布,从而降低超顺磁聚类方法的计算量.在复杂红外图像上的分割实验表明,新算法在收敛速度和分割效果方面都明显优于经典的SW算法.

Segmentation algorithm for complicated infrared image based on superparamagnetic clustering

Inspired by the thermodynamic aggregation motion of inhomogeneous Potts model as data clustering,a new segmentation method based on superparamagnetic clustering is proposed for segmenting complicated infrared image. First,the Hamiltonian function for controlling system action is determined. Then,the system's phase is recognized by measuring the curve of susceptibility vs temperature. Finally, the image is segmented into sub-clusters by measuring the spin-spin correlation function in the superparamagnetic phase. Combined the SW algorithm and Metropolis algorithm, a new method for generating Markov process is proposed, which can converged into Boltzmann distribution quickly,so reducing the computation time of superparamagnetic clustering. The experimental results for complicated infrared images show that the proposed method is obviously better than the SW algorithm in the aspect of convergence speed and segmentation effects.