Fast segmentation approach for SAR image based on simple Markov random field

Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvan-tages are addressed, and a fast segmentation approach based on simple Markov random field (MRF) for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.

MRF model and FRAME model-based unsupervised image segmentation

This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses "Filters, Random and Maximum Entropy (Abb. FRAME)" model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.

一种基于简化马尔可夫随机场的红外图像快速分割方法

分析了传统的基于马尔可夫随机场图像分割算法收敛速度慢和固定加权等缺点,提出了一种基于简化马尔可夫随机场的红外图像快速分割算法。该算法首先对红外图像极大似然初始分割,并利用简化马尔可夫随机场对图像进行建模。在此基础上采用自适应的加权变化形式进行迭代,不但加速了分割算法的收敛速度,而且使得分割效果都大为改善。在真实的飞机和舰艇红外图像上,该算法都取得了较好的分割效果。

结合核密度估计理论的ICM遥感影像分割算法

针对传统迭代条件模式算法用于遥感影像分割时易出现误分割的问题,该文提出了结合核密度估计理论的迭代条件模式算法。使用自适应双边滤波器对影像进行预处理以提高影像质量,运用基于核密度估计理论的爬山算法获取影像的初始标记,结合MAP-MRF框架构建一种新的ICM算法对遥感影像进行分割。实验结果表明,使用基于核密度估计理论的爬山算法获取的初始标记后,基于MAP-MRF框架构建的分割算法能得到更准确的分割结果。和已有算法相比,该算法获得的分割结果在准确率和Kappa系数上都优于传统ICM算法、基于丰富语义的ICM算法和改进的ICM算法。