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.
