In this paper, a novel approach is proposed for unsupervised change detection of multitemporal remote sensing images. The proposed method is able to produce the change detection result on the difference image without ...
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In this paper, Reversible logic circuit synthesis is formulated as a quantum cost-minimization problem with equality constraint. A new reference-point based evolutionary multi-objective method R-EMO-RLC is specially d...
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We have proposed multi-objective Gene Expression Programming (GEP) based automatic clustering method (do not need prior knowledge), which is denoted as MOGEPC. In our algorithm, we adopt GEP based multi-objective opti...
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Considering the shortage of edge preservation and low direction-resolution for SAR image segmentation based on the conventional wavelet transform domain, a new segmentation method is proposed based on Gray-Level Coocc...
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Considering the shortage of edge preservation and low direction-resolution for SAR image segmentation based on the conventional wavelet transform domain, a new segmentation method is proposed based on Gray-Level Cooccurrence Probability (GLCP) features in the overcomplete Brushlet domain. This method compresses the redundant GLCP features extracted by the adaptive window Gabor filtering in different direction coefficient blocks using compressed sensing, then the Fuzzy C-Mean (FCM) clustering method is utilized to complete the clustering and obtain the segmentation result. The experiment results show that the new method has advantages in the edge preservation and direction extraction, and obtains better segmentation results with respect to other methods.
Objectives: Assessing patients’ responses to Transarterial Chemoembolization (TACE) treatment by analyzing the volume changes of Hepatocellular Carcinomas (HCC) is critical for treatment planning and prognosis. To ob...
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When Multi-objective Particle Swarm Optimization (MOPSO) optimizes the multi-objective problems of the multiobjective simultaneous learning framework (MSCC), there are only a few nondominated solutions in MOPSO popula...
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When Multi-objective Particle Swarm Optimization (MOPSO) optimizes the multi-objective problems of the multiobjective simultaneous learning framework (MSCC), there are only a few nondominated solutions in MOPSO population. In this case, NSGA-II can keep a lot of good dominated solutions in the population, which will help the population optimize, so this paper brought in NSGA-II as the optimization algorithm. The results of experiments show that, under the optimization of NSGA-II, MSCC framework can get better multi-class classifiers. However, dominated solutions can get better classifiers than nondominated solutions. By observing the changing curves of the maximum classification accuracy rate following with the optimization of populations, this paper found that, when dealing with most of the data sets, the maximum accuracy is not improved following the optimization of populations.
Directionlet transform can capture the image singularity due to possessing the multi-direction anisotropic basis functions. A texture classification algorithm based on the Mapping complex Directionlet Transform (M-DT)...
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Directionlet transform can capture the image singularity due to possessing the multi-direction anisotropic basis functions. A texture classification algorithm based on the Mapping complex Directionlet Transform (M-DT) is proposed, which provides better directionality and approximate shift invariance. By space mapping for the texture image, then complex Directionlet transform is applied to the mapped image, and the multiscale subband coefficient energy feature is used for texture classification. The experiments using texture images from Brodatz and real SAR images indicate the proposed method outperforms wavelets and Multiscale Geometric Analysis (MGA) approaches, the potential application to image analysis by Directionlet is thus proved.
This paper studies pinning consensus of networks with time-varying topology via impulsive control. Event-triggered rules are employed in diffusion coupling terms to reduce the updating load of the coupled system. Suff...
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ISBN:
(纸本)9781509009107
This paper studies pinning consensus of networks with time-varying topology via impulsive control. Event-triggered rules are employed in diffusion coupling terms to reduce the updating load of the coupled system. Sufficient conditions to pin the network to a prescribed value are proposed. It is proved that in case the coupling matrix across time intervals with length less than some given constant has spanning trees, then the proposed algorithms can realize consensus on the prescribed value when the impulsive controller is imposed randomly on one agent with appropriate impulsive strength and impulse intervals. The imposed pulses can be very sparse, with the impulsive intervals being lower bounded. Two kinds of event-triggered schemes coexist in this pinning control algorithm, one for updating the coupling diffusions;one for applying the impulsive control. Examples with numerical simulation are provided to show the effectiveness of the theoretical results.
Community detection has great applications in many areas. Many algorithms have been proposed to solve this problem, while these algorithms could not detect communities in large-scale networks effectively and efficient...
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A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differenti...
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A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differential equation. A new function is proposed to characterize the population of excitatory and inhibitory spiking neurons, which is different from the population density function discussed by most researchers. And a novel evolution equation, which is a nonhomogeneous parabolic type equation, is derived. From this, the stationary solution and the firing rate of the stationary states are given. Last, by the Fourier transform, the time dependent solution is also obtained. This method can be used to analyze the various dynamic behaviors of neuronal populations.
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