针对传统的迭代条件模式(iterated conditional model,ICM)算法应用于遥感影像分割时容易出现离散斑块和孤立点的问题,提出了一种基于马尔科夫随机场(Markov random field,MRF)的改进ICM遥感影像分割算法。首先,在获取初始标记之前加入...针对传统的迭代条件模式(iterated conditional model,ICM)算法应用于遥感影像分割时容易出现离散斑块和孤立点的问题,提出了一种基于马尔科夫随机场(Markov random field,MRF)的改进ICM遥感影像分割算法。首先,在获取初始标记之前加入保边去噪效果良好的双边滤波器(bilateral filter,BF),用于遥感影像的预处理;并用多阈值最大类间方差法(Otsu)获取初始标记,以克服传统的初始标记获取算法中K-means聚类算法类别数不确定和算法复杂度不易控制以及错分现象明显等问题;然后,利用MRF描述像元的空间相关性,形成顾及上下文信息的ICM遥感影像分割算法。通过遥感影像数据分割实例验证,所提方法的分割精度优于传统的ICM算法。展开更多
利用上下文马尔可夫随机场(Markov Random Field,MRF)模型,将图像分类问题转化为能量函数最小化(最优化)问题。该方法构建了MRF关于彩色街景图像的先验观测场模型,并利用迭代条件模式(Iterated Conditional Model,ICM)算法获得后验标记...利用上下文马尔可夫随机场(Markov Random Field,MRF)模型,将图像分类问题转化为能量函数最小化(最优化)问题。该方法构建了MRF关于彩色街景图像的先验观测场模型,并利用迭代条件模式(Iterated Conditional Model,ICM)算法获得后验标记场能量最小。通过和模糊C均值(Fuzzy C-means,FCM)算法实验对比表明,该方法不仅能有效分类,而且分类精度要远高于FCM。展开更多
In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the bas...In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.展开更多
This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and ...This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field(MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes(ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61172047)
文摘In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.
基金Argentinean National Council for Scientific Research (CONICET)the National University of San Juan (UNSJ) of ArgentinaNVIDIA Corporation for their support
文摘This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field(MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes(ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.
